An adaptive deep learning model for noisy image super-resolution

ABSTRACT

Embodiments described herein are generally directed to an end-to-end trainable degradation restoration network (DRN) that enhances the ability of a super-resolution (SR) subnetwork to deal with noisy low-resolution images. An embodiment of a method includes estimating, by a noise estimator (NE) subnetwork of the DRN, an estimated noise map for a noisy input image; and predicting, by the SR subnetwork of the DRN, a clean upscaled image based on the input image and the noise map by, for each of multiple conditional residual dense blocks (CRDBs) stacked within one or more cascade blocks representing the SR subnetwork, adjusting, by a noise control layer of the CRDB that follows a stacked set of a multiple residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the input image by applying (i) a scaling factor and (ii) an offset factor derived from the noise map.

CROSS-REFERENCE TO RELATED APPLICATION

The present application claims, under 35 U.S.C. § 371, the benefit of and priority to International Application No. PCT/CN2019/092228, filed Jun. 21, 2019, titled AN ADAPTIVE DEEP LEARNING MODEL FOR NOISY IMAGE SUPER-RESOLUTION, which is hereby incorporated by reference in its entirety for all purposes.

TECHNICAL FIELD

Embodiments described herein generally relate to the field of image denoising and super-resolution (SR) and, more particularly, to an end-to-end trainable degradation restoration network (DRN), in which an SR convolutional neural network (CNN) subnetwork receives an estimated noise map generated by a noise estimator (NE) CNN subnetwork as guidance to adjust intermediate image features.

BACKGROUND

Image restoration and image enhancement are computer vision tasks, focused on the restoration of degraded image content or the filling in of missing information. In recent years there has been an increased interest on the part of the vision and graphics communities in these fundamental topics of research.

Image denoising and single image super-resolution (SISR) are both low-level image restoration tasks. Image denoising generally refers to a process in which the underlying goal is to estimate the original image by suppressing noise from a noise-contaminated version of the image. Image super-resolution (SR) generally refers to a class of techniques that attempt to reconstruct a higher-resolution image or sequence from one or more lower-resolution input images.

Deep convolutional neural networks (CNNs) have led to dramatic progress in image denoising and SR in the past few years; however, conventional image restoration approaches learn these two tasks separately, resulting in difficulties when attempting to combine these two tasks together. Furthermore, most conventional SR networks are trained with low-resolution (LR) images that have been directly downsampled from high-resolution (HR) counterparts. As such, another limitation of existing SR networks is that they tend to perform poorly on real-world LR images that are not simply acquired by a downsampler and that may include unknown degradation (e.g., sensor noise, Joint Photographic Experts Group (JPEG) compression and the like). Moreover, existing SR networks lack the scalability to deal with images having different noise levels and compression qualities.

BRIEF DESCRIPTION OF THE DRAWINGS

Embodiments described here are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings in which like reference numerals refer to similar elements.

FIG. 1 is a block diagram of a processing system, according to an embodiment.

FIGS. 2A-2D illustrate computing systems and graphics processors, according to some embodiments.

FIGS. 3A-3C illustrate block diagrams of additional graphics processor and compute accelerator architectures, according to some embodiments.

FIG. 4 is a block diagram of a graphics processing engine of a graphics processor, according to some embodiments.

FIGS. 5A-5B illustrate thread execution logic including an array of processing elements employed in a graphics processor core, according to some embodiments.

FIG. 6 illustrates an additional execution unit, according to an embodiment.

FIG. 7 is a block diagram illustrating graphics processor instruction formats, according to some embodiments.

FIG. 8 is a block diagram of another embodiment of a graphics processor.

FIG. 9A is a block diagram illustrating a graphics processor command format, according to some embodiments.

FIG. 9B is a block diagram illustrating a graphics processor command sequence, according to an embodiment.

FIG. 10 illustrates an exemplary graphics software architecture for a data processing system, according to some embodiments.

FIG. 11A is a block diagram illustrating an IP core development system that may be used to manufacture an integrated circuit to perform operations, according to an embodiment.

FIG. 11B illustrates a cross-section side view of an integrated circuit package assembly, according to some embodiments.

FIG. 11C illustrates a package assembly that includes multiple units of hardware logic chiplets connected to a substrate, according to an embodiment.

FIG. 11D illustrates a package assembly including interchangeable chiplets, according to an embodiment.

FIG. 12 is a block diagram illustrating an exemplary system on a chip integrated circuit that may be fabricated using one or more IP cores, according to an embodiment.

FIGS. 13A-13B are block diagrams illustrating exemplary graphics processors for use within an SoC, according to some embodiments.

FIG. 14 illustrates a machine learning software stack, according to an embodiment.

FIG. 15A-15B illustrate layers of exemplary deep neural networks.

FIG. 16 illustrates an exemplary recurrent neural network.

FIG. 17 illustrates training and deployment of a deep neural network.

FIG. 18 is a block diagram illustrating distributed learning.

FIG. 19 is a flow diagram illustrating degradation restoration network (DRN) inference processing, according to an embodiment.

FIG. 20A is a block diagram illustrating a conditional residual dense block (CRDB), according to an embodiment.

FIG. 20B is a block diagram illustrating a noise control layer (NCL) of a CRDB, according to an embodiment.

FIG. 21 is a block diagram illustrating a degradation restoration network (DRN), according to an embodiment.

FIG. 22A is a block diagram illustrating fine tuning of a degradation restoration network (DRN) with noise shifting, according to an embodiment.

FIG. 22B is a block diagram illustrating a noise shifting subnetwork, according to an embodiment.

FIG. 23 is a flow diagram illustrating a first phase of degradation restoration network (DRN) training processing, according to an embodiment.

FIG. 24 is a flow diagram illustrating a second phase of degradation restoration network (DRN) training processing, according to an embodiment.

FIG. 25 is a table listing Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics for image super-resolution performed by various models on well-known test datasets.

FIG. 26 is a table 2600 listing PSNR and SSIM metrics for the Darmstadt Noise Dataset (DND) benchmark photo denoising test results achieved by the top five models to date.

FIG. 27 is a table listing PSNR and SSIM metrics for noisy image super-resolution performed by various models on well-known test datasets to which synthetic noise was added.

FIG. 28 provides a visual comparison of results of noisy image super-resolution on a sample image of the BSD100 dataset by various models, including a degradation restoration network (DRN) in accordance with an embodiment.

FIG. 29 provides a visual comparison of results of noisy image super-resolution on another sample image of the BSD100 dataset by various models, including a degradation restoration network (DRN) in accordance with an embodiment.

FIG. 30 provides a visual comparison of results of noisy image super-resolution on another sample image of the BSD100 dataset by various models, including a degradation restoration network (DRN) in accordance with an embodiment.

FIG. 31 provides a visual comparison of results of noisy image super-resolution on a sample image from the Set5 dataset by various models, including a degradation restoration network (DRN) in accordance with an embodiment.

FIG. 32 provides a visual comparison of results of noisy image super-resolution on a sample image from the Urban100 dataset by various models, including a degradation restoration network (DRN) in accordance with an embodiment.

DETAILED DESCRIPTION

Embodiments described herein are generally directed to an end-to-end trainable degradation restoration network (DRN) that enhances the ability of a super-resolution (SR) subnetwork to deal with noisy low-resolution (LR) images.

In some embodiments, an improved approach to super-resolving noisy images (e.g., LR images having unknown degradation, including, but not limited to, sensor noise and/or JPEG compression) involves input of an estimated noise map to a super-resolution (SR) convolutional neural network (CNN) subnetwork (which may also be referred to herein simply as an SR subnetwork or as a conditional SR subnetwork) of the DRN. The estimated noise map may be generated by a noise estimator (NE) CNN subnetwork (which may also be referred to herein simply as an NE subnetwork) and may be used by the SR subnetwork as guidance to adjust intermediate image features during inference processing, thereby enhancing the ability of the SR network to deal with noisy images.

System Overview

FIG. 1 is a block diagram of a processing system 100, according to an embodiment. System 100 may be used in a single processor desktop system, a multiprocessor workstation system, or a server system having a large number of processors 102 or processor cores 107. In one embodiment, the system 100 is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices such as within Internet-of-things (IoT) devices with wired or wireless connectivity to a local or wide area network.

In one embodiment, system 100 can include, couple with, or be integrated within: a server-based gaming platform; a game console, including a game and media console; a mobile gaming console, a handheld game console, or an online game console. In some embodiments the system 100 is part of a mobile phone, smart phone, tablet computing device or mobile Internet-connected device such as a laptop with low internal storage capacity. Processing system 100 can also include, couple with, or be integrated within: a wearable device, such as a smart watch wearable device; smart eyewear or clothing enhanced with augmented reality (AR) or virtual reality (VR) features to provide visual, audio or tactile outputs to supplement real world visual, audio or tactile experiences or otherwise provide text, audio, graphics, video, holographic images or video, or tactile feedback; other augmented reality (AR) device; or other virtual reality (VR) device. In some embodiments, the processing system 100 includes or is part of a television or set top box device. In one embodiment, system 100 can include, couple with, or be integrated within a self-driving vehicle such as a bus, tractor trailer, car, motor or electric power cycle, plane or glider (or any combination thereof). The self-driving vehicle may use system 100 to process the environment sensed around the vehicle.

In some embodiments, the one or more processors 102 each include one or more processor cores 107 to process instructions which, when executed, perform operations for system or user software. In some embodiments, at least one of the one or more processor cores 107 is configured to process a specific instruction set 109. In some embodiments, instruction set 109 may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computing via a Very Long Instruction Word (VLIW). One or more processor cores 107 may process a different instruction set 109, which may include instructions to facilitate the emulation of other instruction sets. Processor core 107 may also include other processing devices, such as a Digital Signal Processor (DSP).

In some embodiments, the processor 102 includes cache memory 104. Depending on the architecture, the processor 102 can have a single internal cache or multiple levels of internal cache. In some embodiments, the cache memory is shared among various components of the processor 102. In some embodiments, the processor 102 also uses an external cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which may be shared among processor cores 107 using known cache coherency techniques. A register file 106 can be additionally included in processor 102 and may include different types of registers for storing different types of data (e.g., integer registers, floating point registers, status registers, and an instruction pointer register). Some registers may be general-purpose registers, while other registers may be specific to the design of the processor 102.

In some embodiments, one or more processor(s) 102 are coupled with one or more interface bus(es) 110 to transmit communication signals such as address, data, or control signals between processor 102 and other components in the system 100. The interface bus 110, in one embodiment, can be a processor bus, such as a version of the Direct Media Interface (DMI) bus. However, processor busses are not limited to the DMI bus, and may include one or more Peripheral Component Interconnect buses (e.g., PCI, PCI express), memory busses, or other types of interface busses. In one embodiment the processor(s) 102 include an integrated memory controller 116 and a platform controller hub 130. The memory controller 116 facilitates communication between a memory device and other components of the system 100, while the platform controller hub (PCH) 130 provides connections to I/O devices via a local I/O bus.

The memory device 120 can be a dynamic random-access memory (DRAM) device, a static random-access memory (SRAM) device, flash memory device, phase-change memory device, or some other memory device having suitable performance to serve as process memory. In one embodiment the memory device 120 can operate as system memory for the system 100, to store data 122 and instructions 121 for use when the one or more processors 102 executes an application or process. Memory controller 116 also couples with an optional external graphics processor 118, which may communicate with the one or more graphics processors 108 in processors 102 to perform graphics and media operations. In some embodiments, graphics, media, and or compute operations may be assisted by an accelerator 112 which is a coprocessor that can be configured to perform a specialized set of graphics, media, or compute operations. For example, in one embodiment the accelerator 112 is a matrix multiplication accelerator used to optimize machine learning or compute operations. In one embodiment the accelerator 112 is a ray-tracing accelerator that can be used to perform ray-tracing operations in concert with the graphics processor 108. In one embodiment, an external accelerator 119 may be used in place of or in concert with the accelerator 112.

In some embodiments a display device 111 can connect to the processor(s) 102. The display device 111 can be one or more of an internal display device, as in a mobile electronic device or a laptop device or an external display device attached via a display interface (e.g., DisplayPort, etc.). In one embodiment the display device 111 can be a head mounted display (HMD) such as a stereoscopic display device for use in virtual reality (VR) applications or augmented reality (AR) applications.

In some embodiments the platform controller hub 130 enables peripherals to connect to memory device 120 and processor 102 via a high-speed I/O bus. The I/O peripherals include, but are not limited to, an audio controller 146, a network controller 134, a firmware interface 128, a wireless transceiver 126, touch sensors 125, a data storage device 124 (e.g., non-volatile memory, volatile memory, hard disk drive, flash memory, NAND, 3D NAND, 3D XPoint, etc.). The data storage device 124 can connect via a storage interface (e.g., SATA) or via a peripheral bus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCI express). The touch sensors 125 can include touch screen sensors, pressure sensors, or fingerprint sensors. The wireless transceiver 126 can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver such as a 3G, 4G, 5G, or Long-Term Evolution (LTE) transceiver. The firmware interface 128 enables communication with system firmware, and can be, for example, a unified extensible firmware interface (UEFI). The network controller 134 can enable a network connection to a wired network. In some embodiments, a high-performance network controller (not shown) couples with the interface bus 110. The audio controller 146, in one embodiment, is a multi-channel high definition audio controller. In one embodiment the system 100 includes an optional legacy I/O controller 140 for coupling legacy (e.g., Personal System 2 (PS/2)) devices to the system. The platform controller hub 130 can also connect to one or more Universal Serial Bus (USB) controllers 142 connect input devices, such as keyboard and mouse 143 combinations, a camera 144, or other USB input devices.

It will be appreciated that the system 100 shown is exemplary and not limiting, as other types of data processing systems that are differently configured may also be used. For example, an instance of the memory controller 116 and platform controller hub 130 may be integrated into a discreet external graphics processor, such as the external graphics processor 118. In one embodiment the platform controller hub 130 and/or memory controller 116 may be external to the one or more processor(s) 102. For example, the system 100 can include an external memory controller 116 and platform controller hub 130, which may be configured as a memory controller hub and peripheral controller hub within a system chipset that is in communication with the processor(s) 102.

For example, circuit boards (“sleds”) can be used on which components such as CPUs, memory, and other components are placed are designed for increased thermal performance. In some examples, processing components such as the processors are located on a top side of a sled while near memory, such as DIMMs, are located on a bottom side of the sled. As a result of the enhanced airflow provided by this design, the components may operate at higher frequencies and power levels than in typical systems, thereby increasing performance. Furthermore, the sleds are configured to blindly mate with power and data communication cables in a rack, thereby enhancing their ability to be quickly removed, upgraded, reinstalled, and/or replaced. Similarly, individual components located on the sleds, such as processors, accelerators, memory, and data storage drives, are configured to be easily upgraded due to their increased spacing from each other. In the illustrative embodiment, the components additionally include hardware attestation features to prove their authenticity.

A data center can utilize a single network architecture (“fabric”) that supports multiple other network architectures including Ethernet and Omni-Path. The sleds can be coupled to switches via optical fibers, which provide higher bandwidth and lower latency than typical twisted pair cabling (e.g., Category 5, Category 5e, Category 6, etc.). Due to the high bandwidth, low latency interconnections and network architecture, the data center may, in use, pool resources, such as memory, accelerators (e.g., GPUs, graphics accelerators, FPGAs, ASICs, neural network and/or artificial intelligence accelerators, etc.), and data storage drives that are physically disaggregated, and provide them to compute resources (e.g., processors) on an as needed basis, enabling the compute resources to access the pooled resources as if they were local.

A power supply or source can provide voltage and/or current to system 100 or any component or system described herein. In one example, the power supply includes an AC to DC (alternating current to direct current) adapter to plug into a wall outlet. Such AC power can be renewable energy (e.g., solar power) power source. In one example, power source includes a DC power source, such as an external AC to DC converter. In one example, power source or power supply includes wireless charging hardware to charge via proximity to a charging field. In one example, power source can include an internal battery, alternating current supply, motion-based power supply, solar power supply, or fuel cell source.

FIGS. 2A-2D illustrate computing systems and graphics processors provided by embodiments described herein. The elements of FIGS. 2A-2D having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such.

FIG. 2A is a block diagram of an embodiment of a processor 200 having one or more processor cores 202A-202N, an integrated memory controller 214, and an integrated graphics processor 208. Processor 200 can include additional cores up to and including additional core 202N represented by the dashed lined boxes. Each of processor cores 202A-202N includes one or more internal cache units 204A-204N. In some embodiments each processor core also has access to one or more shared cached units 206. The internal cache units 204A-204N and shared cache units 206 represent a cache memory hierarchy within the processor 200. The cache memory hierarchy may include at least one level of instruction and data cache within each processor core and one or more levels of shared mid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, where the highest level of cache before external memory is classified as the LLC. In some embodiments, cache coherency logic maintains coherency between the various cache units 206 and 204A-204N.

In some embodiments, processor 200 may also include a set of one or more bus controller units 216 and a system agent core 210. The one or more bus controller units 216 manage a set of peripheral buses, such as one or more PCI or PCI express busses. System agent core 210 provides management functionality for the various processor components. In some embodiments, system agent core 210 includes one or more integrated memory controllers 214 to manage access to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 202A-202N include support for simultaneous multi-threading. In such embodiment, the system agent core 210 includes components for coordinating and operating cores 202A-202N during multi-threaded processing. System agent core 210 may additionally include a power control unit (PCU), which includes logic and components to regulate the power state of processor cores 202A-202N and graphics processor 208.

In some embodiments, processor 200 additionally includes graphics processor 208 to execute graphics processing operations. In some embodiments, the graphics processor 208 couples with the set of shared cache units 206, and the system agent core 210, including the one or more integrated memory controllers 214. In some embodiments, the system agent core 210 also includes a display controller 211 to drive graphics processor output to one or more coupled displays. In some embodiments, display controller 211 may also be a separate module coupled with the graphics processor via at least one interconnect, or may be integrated within the graphics processor 208.

In some embodiments, a ring-based interconnect unit 212 is used to couple the internal components of the processor 200. However, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques, including techniques well known in the art. In some embodiments, graphics processor 208 couples with the ring interconnect 212 via an I/O link 213.

The exemplary I/O link 213 represents at least one of multiple varieties of I/O interconnects, including an on package I/O interconnect which facilitates communication between various processor components and a high-performance embedded memory module 218, such as an eDRAM module. In some embodiments, each of the processor cores 202A-202N and graphics processor 208 can use embedded memory modules 218 as a shared Last Level Cache.

In some embodiments, processor cores 202A-202N are homogenous cores executing the same instruction set architecture. In another embodiment, processor cores 202A-202N are heterogeneous in terms of instruction set architecture (ISA), where one or more of processor cores 202A-202N execute a first instruction set, while at least one of the other cores executes a subset of the first instruction set or a different instruction set.

In one embodiment, processor cores 202A-202N are heterogeneous in terms of microarchitecture, where one or more cores having a relatively higher power consumption couple with one or more power cores having a lower power consumption. In one embodiment, processor cores 202A-202N are heterogeneous in terms of computational capability. Additionally, processor 200 can be implemented on one or more chips or as an SoC integrated circuit having the illustrated components, in addition to other components.

FIG. 2B is a block diagram of hardware logic of a graphics processor core 219, according to some embodiments described herein. Elements of FIG. 2B having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. The graphics processor core 219, sometimes referred to as a core slice, can be one or multiple graphics cores within a modular graphics processor. The graphics processor core 219 is exemplary of one graphics core slice, and a graphics processor as described herein may include multiple graphics core slices based on target power and performance envelopes. Each graphics processor core 219 can include a fixed function block 230 coupled with multiple sub-cores 221A-221F, also referred to as sub-slices, that include modular blocks of general-purpose and fixed function logic.

In some embodiments, the fixed function block 230 includes a geometry/fixed function pipeline 231 that can be shared by all sub-cores in the graphics processor core 219, for example, in lower performance and/or lower power graphics processor implementations. In various embodiments, the geometry/fixed function pipeline 231 includes a 3D fixed function pipeline (e.g., 3D pipeline 312 as in FIG. 3 and FIG. 4, described below) a video front-end unit, a thread spawner and thread dispatcher, and a unified return buffer manager, which manages unified return buffers (e.g., unified return buffer 418 in FIG. 4, as described below).

In one embodiment the fixed function block 230 also includes a graphics SoC interface 232, a graphics microcontroller 233, and a media pipeline 234. The graphics SoC interface 232 provides an interface between the graphics processor core 219 and other processor cores within a system on a chip integrated circuit. The graphics microcontroller 233 is a programmable sub-processor that is configurable to manage various functions of the graphics processor core 219, including thread dispatch, scheduling, and pre-emption. The media pipeline 234 (e.g., media pipeline 316 of FIG. 3 and FIG. 4) includes logic to facilitate the decoding, encoding, pre-processing, and/or post-processing of multimedia data, including image and video data. The media pipeline 234 implement media operations via requests to compute or sampling logic within the sub-cores 221-221F.

In one embodiment the SoC interface 232 enables the graphics processor core 219 to communicate with general-purpose application processor cores (e.g., CPUs) and/or other components within an SoC, including memory hierarchy elements such as a shared last level cache memory, the system RAM, and/or embedded on-chip or on-package DRAM. The SoC interface 232 can also enable communication with fixed function devices within the SoC, such as camera imaging pipelines, and enables the use of and/or implements global memory atomics that may be shared between the graphics processor core 219 and CPUs within the SoC. The SoC interface 232 can also implement power management controls for the graphics processor core 219 and enable an interface between a clock domain of the graphic core 219 and other clock domains within the SoC. In one embodiment the SoC interface 232 enables receipt of command buffers from a command streamer and global thread dispatcher that are configured to provide commands and instructions to each of one or more graphics cores within a graphics processor. The commands and instructions can be dispatched to the media pipeline 234, when media operations are to be performed, or a geometry and fixed function pipeline (e.g., geometry and fixed function pipeline 231, geometry and fixed function pipeline 237) when graphics processing operations are to be performed.

The graphics microcontroller 233 can be configured to perform various scheduling and management tasks for the graphics processor core 219. In one embodiment the graphics microcontroller 233 can perform graphics and/or compute workload scheduling on the various graphics parallel engines within execution unit (EU) arrays 222A-222F, 224A-224F within the sub-cores 221A-221F. In this scheduling model, host software executing on a CPU core of an SoC including the graphics processor core 219 can submit workloads one of multiple graphic processor doorbells, which invokes a scheduling operation on the appropriate graphics engine. Scheduling operations include determining which workload to run next, submitting a workload to a command streamer, pre-empting existing workloads running on an engine, monitoring progress of a workload, and notifying host software when a workload is complete. In one embodiment the graphics microcontroller 233 can also facilitate low-power or idle states for the graphics processor core 219, providing the graphics processor core 219 with the ability to save and restore registers within the graphics processor core 219 across low-power state transitions independently from the operating system and/or graphics driver software on the system.

The graphics processor core 219 may have greater than or fewer than the illustrated sub-cores 221A-221F, up to N modular sub-cores. For each set of N sub-cores, the graphics processor core 219 can also include shared function logic 235, shared and/or cache memory 236, a geometry/fixed function pipeline 237, as well as additional fixed function logic 238 to accelerate various graphics and compute processing operations. The shared function logic 235 can include logic units associated with the shared function logic 420 of FIG. 4 (e.g., sampler, math, and/or inter-thread communication logic) that can be shared by each N sub-cores within the graphics processor core 219. The shared and/or cache memory 236 can be a last-level cache for the set of N sub-cores 221A-221F within the graphics processor core 219, and can also serve as shared memory that is accessible by multiple sub-cores. The geometry/fixed function pipeline 237 can be included instead of the geometry/fixed function pipeline 231 within the fixed function block 230 and can include the same or similar logic units.

In one embodiment the graphics processor core 219 includes additional fixed function logic 238 that can include various fixed function acceleration logic for use by the graphics processor core 219. In one embodiment the additional fixed function logic 238 includes an additional geometry pipeline for use in position only shading. In position-only shading, two geometry pipelines exist, the full geometry pipeline within the geometry/fixed function pipeline 238, 231, and a cull pipeline, which is an additional geometry pipeline which may be included within the additional fixed function logic 238. In one embodiment the cull pipeline is a trimmed down version of the full geometry pipeline. The full pipeline and the cull pipeline can execute different instances of the same application, each instance having a separate context. Position only shading can hide long cull runs of discarded triangles, enabling shading to be completed earlier in some instances. For example and in one embodiment the cull pipeline logic within the additional fixed function logic 238 can execute position shaders in parallel with the main application and generally generates critical results faster than the full pipeline, as the cull pipeline fetches and shades only the position attribute of the vertices, without performing rasterization and rendering of the pixels to the frame buffer. The cull pipeline can use the generated critical results to compute visibility information for all the triangles without regard to whether those triangles are culled. The full pipeline (which in this instance may be referred to as a replay pipeline) can consume the visibility information to skip the culled triangles to shade only the visible triangles that are finally passed to the rasterization phase.

In one embodiment the additional fixed function logic 238 can also include machine-learning acceleration logic, such as fixed function matrix multiplication logic, for implementations including optimizations for machine learning training or inferencing.

Within each graphics sub-core 221A-221F includes a set of execution resources that may be used to perform graphics, media, and compute operations in response to requests by graphics pipeline, media pipeline, or shader programs. The graphics sub-cores 221A-221F include multiple EU arrays 222A-222F, 224A-224F, thread dispatch and inter-thread communication (TD/IC) logic 223A-223F, a 3D (e.g., texture) sampler 225A-225F, a media sampler 206A-206F, a shader processor 227A-227F, and shared local memory (SLM) 228A-228F. The EU arrays 222A-222F, 224A-224F each include multiple execution units, which are general-purpose graphics processing units capable of performing floating-point and integer/fixed-point logic operations in service of a graphics, media, or compute operation, including graphics, media, or compute shader programs. The TD/IC logic 223A-223F performs local thread dispatch and thread control operations for the execution units within a sub-core and facilitate communication between threads executing on the execution units of the sub-core. The 3D sampler 225A-225F can read texture or other 3D graphics related data into memory. The 3D sampler can read texture data differently based on a configured sample state and the texture format associated with a given texture. The media sampler 206A-206F can perform similar read operations based on the type and format associated with media data. In one embodiment, each graphics sub-core 221A-221F can alternately include a unified 3D and media sampler. Threads executing on the execution units within each of the sub-cores 221A-221F can make use of shared local memory 228A-228F within each sub-core, to enable threads executing within a thread group to execute using a common pool of on-chip memory.

FIG. 2C illustrates a graphics processing unit (GPU) 239 that includes dedicated sets of graphics processing resources arranged into multi-core groups 240A-240N. While the details of only a single multi-core group 240A are provided, it will be appreciated that the other multi-core groups 240B-240N may be equipped with the same or similar sets of graphics processing resources.

As illustrated, a multi-core group 240A may include a set of graphics cores 243, a set of tensor cores 244, and a set of ray tracing cores 245. A scheduler/dispatcher 241 schedules and dispatches the graphics threads for execution on the various cores 243, 244, 245. A set of register files 242 store operand values used by the cores 243, 244, 245 when executing the graphics threads. These may include, for example, integer registers for storing integer values, floating point registers for storing floating point values, vector registers for storing packed data elements (integer and/or floating point data elements) and tile registers for storing tensor/matrix values. In one embodiment, the tile registers are implemented as combined sets of vector registers.

One or more combined level 1 (L1) caches and shared memory units 247 store graphics data such as texture data, vertex data, pixel data, ray data, bounding volume data, etc., locally within each multi-core group 240A. One or more texture units 247 can also be used to perform texturing operations, such as texture mapping and sampling. A Level 2 (L2) cache 253 shared by all or a subset of the multi-core groups 240A-240N stores graphics data and/or instructions for multiple concurrent graphics threads. As illustrated, the L2 cache 253 may be shared across a plurality of multi-core groups 240A-240N. One or more memory controllers 248 couple the GPU 239 to a memory 249 which may be a system memory (e.g., DRAM) and/or a dedicated graphics memory (e.g., GDDR6 memory).

Input/output (I/O) circuitry 250 couples the GPU 239 to one or more I/O devices 252 such as digital signal processors (DSPs), network controllers, or user input devices. An on-chip interconnect may be used to couple the I/O devices 252 to the GPU 239 and memory 249. One or more I/O memory management units (IOMMUs) 251 of the I/O circuitry 250 couple the I/O devices 252 directly to the system memory 249. In one embodiment, the IOMMU 251 manages multiple sets of page tables to map virtual addresses to physical addresses in system memory 249. In this embodiment, the I/O devices 252, CPU(s) 246, and GPU(s) 239 may share the same virtual address space.

In one implementation, the IOMMU 251 supports virtualization. In this case, it may manage a first set of page tables to map guest/graphics virtual addresses to guest/graphics physical addresses and a second set of page tables to map the guest/graphics physical addresses to system/host physical addresses (e.g., within system memory 249). The base addresses of each of the first and second sets of page tables may be stored in control registers and swapped out on a context switch (e.g., so that the new context is provided with access to the relevant set of page tables). While not illustrated in FIG. 2C, each of the cores 243, 244, 245 and/or multi-core groups 240A-240N may include translation lookaside buffers (TLBs) to cache guest virtual to guest physical translations, guest physical to host physical translations, and guest virtual to host physical translations.

In one embodiment, the CPUs 246, GPUs 239, and I/O devices 252 are integrated on a single semiconductor chip and/or chip package. The illustrated memory 249 may be integrated on the same chip or may be coupled to the memory controllers 248 via an off-chip interface. In one implementation, the memory 249 comprises GDDR6 memory which shares the same virtual address space as other physical system-level memories, although the underlying principles are not limited to this specific implementation.

In one embodiment, the tensor cores 244 include a plurality of execution units specifically designed to perform matrix operations, which are the fundamental compute operation used to perform deep learning operations. For example, simultaneous matrix multiplication operations may be used for neural network training and inferencing. The tensor cores 244 may perform matrix processing using a variety of operand precisions including single precision floating-point (e.g., 32 bits), half-precision floating point (e.g., 16 bits), integer words (16 bits), bytes (8 bits), and half-bytes (4 bits). In one embodiment, a neural network implementation extracts features of each rendered scene, potentially combining details from multiple frames, to construct a high-quality final image.

In deep learning implementations, parallel matrix multiplication work may be scheduled for execution on the tensor cores 244. The training of neural networks, in particular, requires a significant number matrix dot product operations. In order to process an inner-product formulation of an N×N×N matrix multiply, the tensor cores 244 may include at least N dot-product processing elements. Before the matrix multiply begins, one entire matrix is loaded into tile registers and at least one column of a second matrix is loaded each cycle for N cycles. Each cycle, there are N dot products that are processed.

Matrix elements may be stored at different precisions depending on the particular implementation, including 16-bit words, 8-bit bytes (e.g., INT8) and 4-bit half-bytes (e.g., INT4). Different precision modes may be specified for the tensor cores 244 to ensure that the most efficient precision is used for different workloads (e.g., such as inferencing workloads which can tolerate quantization to bytes and half-bytes).

In one embodiment, the ray tracing cores 245 accelerate ray tracing operations for both real-time ray tracing and non-real-time ray tracing implementations. In particular, the ray tracing cores 245 include ray traversal/intersection circuitry for performing ray traversal using bounding volume hierarchies (BVHs) and identifying intersections between rays and primitives enclosed within the BVH volumes. The ray tracing cores 245 may also include circuitry for performing depth testing and culling (e.g., using a Z buffer or similar arrangement). In one implementation, the ray tracing cores 245 perform traversal and intersection operations in concert with the image denoising techniques described herein, at least a portion of which may be executed on the tensor cores 244. For example, in one embodiment, the tensor cores 244 implement a deep learning neural network to perform denoising of frames generated by the ray tracing cores 245. However, the CPU(s) 246, graphics cores 243, and/or ray tracing cores 245 may also implement all or a portion of the denoising and/or deep learning algorithms.

In addition, as described above, a distributed approach to denoising may be employed in which the GPU 239 is in a computing device coupled to other computing devices over a network or high speed interconnect. In this embodiment, the interconnected computing devices share neural network learning/training data to improve the speed with which the overall system learns to perform denoising for different types of image frames and/or different graphics applications.

In one embodiment, the ray tracing cores 245 process all BVH traversal and ray-primitive intersections, saving the graphics cores 243 from being overloaded with thousands of instructions per ray. In one embodiment, each ray tracing core 245 includes a first set of specialized circuitry for performing bounding box tests (e.g., for traversal operations) and a second set of specialized circuitry for performing the ray-triangle intersection tests (e.g., intersecting rays which have been traversed). Thus, in one embodiment, the multi-core group 240A can simply launch a ray probe, and the ray tracing cores 245 independently perform ray traversal and intersection and return hit data (e.g., a hit, no hit, multiple hits, etc.) to the thread context. The other cores 243, 244 are freed to perform other graphics or compute work while the ray tracing cores 245 perform the traversal and intersection operations.

In one embodiment, each ray tracing core 245 includes a traversal unit to perform BVH testing operations and an intersection unit which performs ray-primitive intersection tests. The intersection unit generates a “hit”, “no hit”, or “multiple hit” response, which it provides to the appropriate thread. During the traversal and intersection operations, the execution resources of the other cores (e.g., graphics cores 243 and tensor cores 244) are freed to perform other forms of graphics work.

In one particular embodiment described below, a hybrid rasterization/ray tracing approach is used in which work is distributed between the graphics cores 243 and ray tracing cores 245.

In one embodiment, the ray tracing cores 245 (and/or other cores 243, 244) include hardware support for a ray tracing instruction set such as Microsoft's DirectX Ray Tracing (DXR) which includes a DispatchRays command, as well as ray-generation, closest-hit, any-hit, and miss shaders, which enable the assignment of unique sets of shaders and textures for each object. Another ray tracing platform which may be supported by the ray tracing cores 245, graphics cores 243 and tensor cores 244 is Vulkan 1.1.85. Note, however, that the underlying principles are not limited to any particular ray tracing ISA.

In general, the various cores 245, 244, 243 may support a ray tracing instruction set that includes instructions/functions for ray generation, closest hit, any hit, ray-primitive intersection, per-primitive and hierarchical bounding box construction, miss, visit, and exceptions. More specifically, one embodiment includes ray tracing instructions to perform the following functions:

Ray Generation—Ray generation instructions may be executed for each pixel, sample, or other user-defined work assignment.

Closest Hit—A closest hit instruction may be executed to locate the closest intersection point of a ray with primitives within a scene.

Any Hit—An any hit instruction identifies multiple intersections between a ray and primitives within a scene, potentially to identify a new closest intersection point.

Intersection—An intersection instruction performs a ray-primitive intersection test and outputs a result.

Per-primitive Bounding box Construction—This instruction builds a bounding box around a given primitive or group of primitives (e.g., when building a new BVH or other acceleration data structure).

Miss—Indicates that a ray misses all geometry within a scene, or specified region of a scene.

Visit—Indicates the children volumes a ray will traverse.

Exceptions—Includes various types of exception handlers (e.g., invoked for various error conditions).

FIG. 2D is a block diagram of general purpose graphics processing unit (GPGPU) 270 that can be configured as a graphics processor and/or compute accelerator, according to embodiments described herein. The GPGPU 270 can interconnect with host processors (e.g., one or more CPU(s) 246) and memory 271, 272 via one or more system and/or memory busses. In one embodiment the memory 271 is system memory that may be shared with the one or more CPU(s) 246, while memory 272 is device memory that is dedicated to the GPGPU 270. In one embodiment, components within the GPGPU 270 and device memory 272 may be mapped into memory addresses that are accessible to the one or more CPU(s) 246. Access to memory 271 and 272 may be facilitated via a memory controller 268. In one embodiment the memory controller 268 includes an internal direct memory access (DMA) controller 269 or can include logic to perform operations that would otherwise be performed by a DMA controller.

The GPGPU 270 includes multiple cache memories, including an L2 cache 253, L1 cache 254, an instruction cache 255, and shared memory 256, at least a portion of which may also be partitioned as a cache memory. The GPGPU 270 also includes multiple compute units 260A-260N. Each compute unit 260A-260N includes a set of vector registers 261, scalar registers 262, vector logic units 263, and scalar logic units 264. The compute units 260A-260N can also include local shared memory 265 and a program counter 266. The compute units 260A-260N can couple with a constant cache 267, which can be used to store constant data, which is data that will not change during the run of kernel or shader program that executes on the GPGPU 270. In one embodiment the constant cache 267 is a scalar data cache and cached data can be fetched directly into the scalar registers 262.

During operation, the one or more CPU(s) 246 can write commands into registers or memory in the GPGPU 270 that has been mapped into an accessible address space. The command processors 257 can read the commands from registers or memory and determine how those commands will be processed within the GPGPU 270. A thread dispatcher 258 can then be used to dispatch threads to the compute units 260A-260N to perform those commands. Each compute unit 260A-260N can execute threads independently of the other compute units. Additionally each compute unit 260A-260N can be independently configured for conditional computation and can conditionally output the results of computation to memory. The command processors 257 can interrupt the one or more CPU(s) 246 when the submitted commands are complete.

FIGS. 3A-3C illustrate block diagrams of additional graphics processor and compute accelerator architectures provided by embodiments described herein. The elements of FIGS. 3A-3C having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such.

FIG. 3A is a block diagram of a graphics processor 300, which may be a discrete graphics processing unit, or may be a graphics processor integrated with a plurality of processing cores, or other semiconductor devices such as, but not limited to, memory devices or network interfaces. In some embodiments, the graphics processor communicates via a memory mapped I/O interface to registers on the graphics processor and with commands placed into the processor memory. In some embodiments, graphics processor 300 includes a memory interface 314 to access memory. Memory interface 314 can be an interface to local memory, one or more internal caches, one or more shared external caches, and/or to system memory.

In some embodiments, graphics processor 300 also includes a display controller 302 to drive display output data to a display device 318. Display controller 302 includes hardware for one or more overlay planes for the display and composition of multiple layers of video or user interface elements. The display device 318 can be an internal or external display device. In one embodiment the display device 318 is a head mounted display device, such as a virtual reality (VR) display device or an augmented reality (AR) display device. In some embodiments, graphics processor 300 includes a video codec engine 306 to encode, decode, or transcode media to, from, or between one or more media encoding formats, including, but not limited to Moving Picture Experts Group (MPEG) formats such as MPEG-2, Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, H.265/HEVC, Alliance for Open Media (AOMedia) VP8, VP9, as well as the Society of Motion Picture & Television Engineers (SMPTE) 421M/VC-1, and Joint Photographic Experts Group (JPEG) formats such as JPEG, and Motion JPEG (MJPEG) formats.

In some embodiments, graphics processor 300 includes a block image transfer (BLIT) engine 304 to perform two-dimensional (2D) rasterizer operations including, for example, bit-boundary block transfers. However, in one embodiment, 2D graphics operations are performed using one or more components of graphics processing engine (GPE) 310. In some embodiments, GPE 310 is a compute engine for performing graphics operations, including three-dimensional (3D) graphics operations and media operations.

In some embodiments, GPE 310 includes a 3D pipeline 312 for performing 3D operations, such as rendering three-dimensional images and scenes using processing functions that act upon 3D primitive shapes (e.g., rectangle, triangle, etc.). The 3D pipeline 312 includes programmable and fixed function elements that perform various tasks within the element and/or spawn execution threads to a 3D/Media sub-system 315. While 3D pipeline 312 can be used to perform media operations, an embodiment of GPE 310 also includes a media pipeline 316 that is specifically used to perform media operations, such as video post-processing and image enhancement.

In some embodiments, media pipeline 316 includes fixed function or programmable logic units to perform one or more specialized media operations, such as video decode acceleration, video de-interlacing, and video encode acceleration in place of, or on behalf of video codec engine 306. In some embodiments, media pipeline 316 additionally includes a thread spawning unit to spawn threads for execution on 3D/Media sub-system 315. The spawned threads perform computations for the media operations on one or more graphics execution units included in 3D/Media sub-system 315.

In some embodiments, 3D/Media subsystem 315 includes logic for executing threads spawned by 3D pipeline 312 and media pipeline 316. In one embodiment, the pipelines send thread execution requests to 3D/Media subsystem 315, which includes thread dispatch logic for arbitrating and dispatching the various requests to available thread execution resources. The execution resources include an array of graphics execution units to process the 3D and media threads. In some embodiments, 3D/Media subsystem 315 includes one or more internal caches for thread instructions and data. In some embodiments, the subsystem also includes shared memory, including registers and addressable memory, to share data between threads and to store output data.

FIG. 3B illustrates a graphics processor 320 having a tiled architecture, according to embodiments described herein. In one embodiment the graphics processor 320 includes a graphics processing engine cluster 322 having multiple instances of the graphics processing engine 310 of FIG. 3A within a graphics engine tile 310A-310D. Each graphics engine tile 310A-310D can be interconnected via a set of tile interconnects 323A-323F. Each graphics engine tile 310A-310D can also be connected to a memory module or memory device 326A-326D via memory interconnects 325A-325D. The memory devices 326A-326D can use any graphics memory technology. For example, the memory devices 326A-326D may be graphics double data rate (GDDR) memory. The memory devices 326A-326D, in one embodiment, are high-bandwidth memory (HBM) modules that can be on-die with their respective graphics engine tile 310A-310D. In one embodiment the memory devices 326A-326D are stacked memory devices that can be stacked on top of their respective graphics engine tile 310A-310D. In one embodiment, each graphics engine tile 310A-310D and associated memory 326A-326D reside on separate chiplets, which are bonded to a base die or base substrate, as described on further detail in FIGS. 11B-11D.

The graphics processing engine cluster 322 can connect with an on-chip or on-package fabric interconnect 324. The fabric interconnect 324 can enable communication between graphics engine tiles 310A-310D and components such as the video codec 306 and one or more copy engines 304. The copy engines 304 can be used to move data out of, into, and between the memory devices 326A-326D and memory that is external to the graphics processor 320 (e.g., system memory). The fabric interconnect 324 can also be used to interconnect the graphics engine tiles 310A-310D. The graphics processor 320 may optionally include a display controller 302 to enable a connection with an external display device 318. The graphics processor may also be configured as a graphics or compute accelerator. In the accelerator configuration, the display controller 302 and display device 318 may be omitted.

The graphics processor 320 can connect to a host system via a host interface 328. The host interface 328 can enable communication between the graphics processor 320, system memory, and/or other system components. The host interface 328 can be, for example a PCI express bus or another type of host system interface.

FIG. 3C illustrates a compute accelerator 330, according to embodiments described herein. The compute accelerator 330 can include architectural similarities with the graphics processor 320 of FIG. 3B and is optimized for compute acceleration. A compute engine cluster 332 can include a set of compute engine tiles 340A-340D that include execution logic that is optimized for parallel or vector-based general-purpose compute operations. In some embodiments, the compute engine tiles 340A-340D do not include fixed function graphics processing logic, although in one embodiment one or more of the compute engine tiles 340A-340D can include logic to perform media acceleration. The compute engine tiles 340A-340D can connect to memory 326A-326D via memory interconnects 325A-325D. The memory 326A-326D and memory interconnects 325A-325D may be similar technology as in graphics processor 320, or can be different. The graphics compute engine tiles 340A-340D can also be interconnected via a set of tile interconnects 323A-323F and may be connected with and/or interconnected by a fabric interconnect 324. In one embodiment the compute accelerator 330 includes a large L3 cache 336 that can be configured as a device-wide cache. The compute accelerator 330 can also connect to a host processor and memory via a host interface 328 in a similar manner as the graphics processor 320 of FIG. 3B.

Graphics Processing Engine

FIG. 4 is a block diagram of a graphics processing engine 410 of a graphics processor in accordance with some embodiments. In one embodiment, the graphics processing engine (GPE) 410 is a version of the GPE 310 shown in FIG. 3A, and may also represent a graphics engine tile 310A-310D of FIG. 3B. Elements of FIG. 4 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. For example, the 3D pipeline 312 and media pipeline 316 of FIG. 3A are illustrated. The media pipeline 316 is optional in some embodiments of the GPE 410 and may not be explicitly included within the GPE 410. For example and in at least one embodiment, a separate media and/or image processor is coupled to the GPE 410.

In some embodiments, GPE 410 couples with or includes a command streamer 403, which provides a command stream to the 3D pipeline 312 and/or media pipelines 316. In some embodiments, command streamer 403 is coupled with memory, which can be system memory, or one or more of internal cache memory and shared cache memory. In some embodiments, command streamer 403 receives commands from the memory and sends the commands to 3D pipeline 312 and/or media pipeline 316. The commands are directives fetched from a ring buffer, which stores commands for the 3D pipeline 312 and media pipeline 316. In one embodiment, the ring buffer can additionally include batch command buffers storing batches of multiple commands. The commands for the 3D pipeline 312 can also include references to data stored in memory, such as but not limited to vertex and geometry data for the 3D pipeline 312 and/or image data and memory objects for the media pipeline 316. The 3D pipeline 312 and media pipeline 316 process the commands and data by performing operations via logic within the respective pipelines or by dispatching one or more execution threads to a graphics core array 414. In one embodiment the graphics core array 414 include one or more blocks of graphics cores (e.g., graphics core(s) 415A, graphics core(s) 415B), each block including one or more graphics cores. Each graphics core includes a set of graphics execution resources that includes general-purpose and graphics specific execution logic to perform graphics and compute operations, as well as fixed function texture processing and/or machine learning and artificial intelligence acceleration logic.

In various embodiments the 3D pipeline 312 can include fixed function and programmable logic to process one or more shader programs, such as vertex shaders, geometry shaders, pixel shaders, fragment shaders, compute shaders, or other shader programs, by processing the instructions and dispatching execution threads to the graphics core array 414. The graphics core array 414 provides a unified block of execution resources for use in processing these shader programs. Multi-purpose execution logic (e.g., execution units) within the graphics core(s) 415A-414B of the graphic core array 414 includes support for various 3D API shader languages and can execute multiple simultaneous execution threads associated with multiple shaders.

In some embodiments, the graphics core array 414 includes execution logic to perform media functions, such as video and/or image processing. In one embodiment, the execution units include general-purpose logic that is programmable to perform parallel general-purpose computational operations, in addition to graphics processing operations. The general-purpose logic can perform processing operations in parallel or in conjunction with general-purpose logic within the processor core(s) 107 of FIG. 1 or core 202A-202N as in FIG. 2A.

Output data generated by threads executing on the graphics core array 414 can output data to memory in a unified return buffer (URB) 418. The URB 418 can store data for multiple threads. In some embodiments the URB 418 may be used to send data between different threads executing on the graphics core array 414. In some embodiments the URB 418 may additionally be used for synchronization between threads on the graphics core array and fixed function logic within the shared function logic 420.

In some embodiments, graphics core array 414 is scalable, such that the array includes a variable number of graphics cores, each having a variable number of execution units based on the target power and performance level of GPE 410. In one embodiment the execution resources are dynamically scalable, such that execution resources may be enabled or disabled as needed.

The graphics core array 414 couples with shared function logic 420 that includes multiple resources that are shared between the graphics cores in the graphics core array. The shared functions within the shared function logic 420 are hardware logic units that provide specialized supplemental functionality to the graphics core array 414. In various embodiments, shared function logic 420 includes but is not limited to sampler 421, math 422, and inter-thread communication (ITC) 423 logic. Additionally, some embodiments implement one or more cache(s) 425 within the shared function logic 420.

A shared function is implemented at least in a case where the demand for a given specialized function is insufficient for inclusion within the graphics core array 414. Instead a single instantiation of that specialized function is implemented as a stand-alone entity in the shared function logic 420 and shared among the execution resources within the graphics core array 414. The precise set of functions that are shared between the graphics core array 414 and included within the graphics core array 414 varies across embodiments. In some embodiments, specific shared functions within the shared function logic 420 that are used extensively by the graphics core array 414 may be included within shared function logic 416 within the graphics core array 414. In various embodiments, the shared function logic 416 within the graphics core array 414 can include some or all logic within the shared function logic 420. In one embodiment, all logic elements within the shared function logic 420 may be duplicated within the shared function logic 416 of the graphics core array 414. In one embodiment the shared function logic 420 is excluded in favor of the shared function logic 416 within the graphics core array 414.

Execution Units

FIGS. 5A-5B illustrate thread execution logic 500 including an array of processing elements employed in a graphics processor core according to embodiments described herein. Elements of FIGS. 5A-5B having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such. FIG. 5A-5B illustrates an overview of thread execution logic 500, which may be representative of hardware logic illustrated with each sub-core 221A-221F of FIG. 2B. FIG. 5A is representative of an execution unit within a general-purpose graphics processor, while FIG. 5B is representative of an execution unit that may be used within a compute accelerator.

As illustrated in FIG. 5A, in some embodiments thread execution logic 500 includes a shader processor 502, a thread dispatcher 504, instruction cache 506, a scalable execution unit array including a plurality of execution units 508A-508N, a sampler 510, shared local memory 511, a data cache 512, and a data port 514. In one embodiment the scalable execution unit array can dynamically scale by enabling or disabling one or more execution units (e.g., any of execution units 508A, 508B, 508C, 508D, through 508N-1 and 508N) based on the computational requirements of a workload. In one embodiment the included components are interconnected via an interconnect fabric that links to each of the components. In some embodiments, thread execution logic 500 includes one or more connections to memory, such as system memory or cache memory, through one or more of instruction cache 506, data port 514, sampler 510, and execution units 508A-508N. In some embodiments, each execution unit (e.g. 508A) is a stand-alone programmable general-purpose computational unit that is capable of executing multiple simultaneous hardware threads while processing multiple data elements in parallel for each thread. In various embodiments, the array of execution units 508A-508N is scalable to include any number individual execution units.

In some embodiments, the execution units 508A-508N are primarily used to execute shader programs. A shader processor 502 can process the various shader programs and dispatch execution threads associated with the shader programs via a thread dispatcher 504. In one embodiment the thread dispatcher includes logic to arbitrate thread initiation requests from the graphics and media pipelines and instantiate the requested threads on one or more execution unit in the execution units 508A-508N. For example, a geometry pipeline can dispatch vertex, tessellation, or geometry shaders to the thread execution logic for processing. In some embodiments, thread dispatcher 504 can also process runtime thread spawning requests from the executing shader programs.

In some embodiments, the execution units 508A-508N support an instruction set that includes native support for many standard 3D graphics shader instructions, such that shader programs from graphics libraries (e.g., Direct 3D and OpenGL) are executed with a minimal translation. The execution units support vertex and geometry processing (e.g., vertex programs, geometry programs, vertex shaders), pixel processing (e.g., pixel shaders, fragment shaders) and general-purpose processing (e.g., compute and media shaders). Each of the execution units 508A-508N is capable of multi-issue single instruction multiple data (SIMD) execution and multi-threaded operation enables an efficient execution environment in the face of higher latency memory accesses. Each hardware thread within each execution unit has a dedicated high-bandwidth register file and associated independent thread-state. Execution is multi-issue per clock to pipelines capable of integer, single and double precision floating point operations, SIMD branch capability, logical operations, transcendental operations, and other miscellaneous operations. While waiting for data from memory or one of the shared functions, dependency logic within the execution units 508A-508N causes a waiting thread to sleep until the requested data has been returned. While the waiting thread is sleeping, hardware resources may be devoted to processing other threads. For example, during a delay associated with a vertex shader operation, an execution unit can perform operations for a pixel shader, fragment shader, or another type of shader program, including a different vertex shader. Various embodiments can apply to use execution by use of Single Instruction Multiple Thread (SIMT) as an alternate to use of SIMD or in addition to use of SIMD. Reference to a SIMD core or operation can apply also to SIMT or apply to SIMD in combination with SIMT.

Each execution unit in execution units 508A-508N operates on arrays of data elements. The number of data elements is the “execution size,” or the number of channels for the instruction. An execution channel is a logical unit of execution for data element access, masking, and flow control within instructions. The number of channels may be independent of the number of physical Arithmetic Logic Units (ALUs) or Floating Point Units (FPUs) for a particular graphics processor. In some embodiments, execution units 508A-508N support integer and floating-point data types.

The execution unit instruction set includes SIMD instructions. The various data elements can be stored as a packed data type in a register and the execution unit will process the various elements based on the data size of the elements. For example, when operating on a 256-bit wide vector, the 256 bits of the vector are stored in a register and the execution unit operates on the vector as four separate 54-bit packed data elements (Quad-Word (QW) size data elements), eight separate 32-bit packed data elements (Double Word (DW) size data elements), sixteen separate 16-bit packed data elements (Word (W) size data elements), or thirty-two separate 8-bit data elements (byte (B) size data elements). However, different vector widths and register sizes are possible.

In one embodiment one or more execution units can be combined into a fused execution unit 509A-509N having thread control logic (507A-507N) that is common to the fused EUs. Multiple EUs can be fused into an EU group. Each EU in the fused EU group can be configured to execute a separate SIMD hardware thread. The number of EUs in a fused EU group can vary according to embodiments. Additionally, various SIMD widths can be performed per-EU, including but not limited to SIMD8, SIMD16, and SIMD32. Each fused graphics execution unit 509A-509N includes at least two execution units. For example, fused execution unit 509A includes a first EU 508A, second EU 508B, and thread control logic 507A that is common to the first EU 508A and the second EU 508B. The thread control logic 507A controls threads executed on the fused graphics execution unit 509A, allowing each EU within the fused execution units 509A-509N to execute using a common instruction pointer register.

One or more internal instruction caches (e.g., 506) are included in the thread execution logic 500 to cache thread instructions for the execution units. In some embodiments, one or more data caches (e.g., 512) are included to cache thread data during thread execution. Threads executing on the execution logic 500 can also store explicitly managed data in the shared local memory 511. In some embodiments, a sampler 510 is included to provide texture sampling for 3D operations and media sampling for media operations. In some embodiments, sampler 510 includes specialized texture or media sampling functionality to process texture or media data during the sampling process before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send thread initiation requests to thread execution logic 500 via thread spawning and dispatch logic. Once a group of geometric objects has been processed and rasterized into pixel data, pixel processor logic (e.g., pixel shader logic, fragment shader logic, etc.) within the shader processor 502 is invoked to further compute output information and cause results to be written to output surfaces (e.g., color buffers, depth buffers, stencil buffers, etc.). In some embodiments, a pixel shader or fragment shader calculates the values of the various vertex attributes that are to be interpolated across the rasterized object. In some embodiments, pixel processor logic within the shader processor 502 then executes an application programming interface (API)-supplied pixel or fragment shader program. To execute the shader program, the shader processor 502 dispatches threads to an execution unit (e.g., 508A) via thread dispatcher 504. In some embodiments, shader processor 502 uses texture sampling logic in the sampler 510 to access texture data in texture maps stored in memory. Arithmetic operations on the texture data and the input geometry data compute pixel color data for each geometric fragment, or discards one or more pixels from further processing.

In some embodiments, the data port 514 provides a memory access mechanism for the thread execution logic 500 to output processed data to memory for further processing on a graphics processor output pipeline. In some embodiments, the data port 514 includes or couples to one or more cache memories (e.g., data cache 512) to cache data for memory access via the data port.

In one embodiment, the execution logic 500 can also include a ray tracer 505 that can provide ray tracing acceleration functionality. The ray tracer 505 can support a ray tracing instruction set that includes instructions/functions for ray generation. The ray tracing instruction set can be similar to or different from the ray-tracing instruction set supported by the ray tracing cores 245 in FIG. 2C.

FIG. 5B illustrates exemplary internal details of an execution unit 508, according to embodiments. A graphics execution unit 508 can include an instruction fetch unit 537, a general register file array (GRF) 524, an architectural register file array (ARF) 526, a thread arbiter 522, a send unit 530, a branch unit 532, a set of SIMD floating point units (FPUs) 534, and in one embodiment a set of dedicated integer SIMD ALUs 535. The GRF 524 and ARF 526 includes the set of general register files and architecture register files associated with each simultaneous hardware thread that may be active in the graphics execution unit 508. In one embodiment, per thread architectural state is maintained in the ARF 526, while data used during thread execution is stored in the GRF 524. The execution state of each thread, including the instruction pointers for each thread, can be held in thread-specific registers in the ARF 526.

In one embodiment the graphics execution unit 508 has an architecture that is a combination of Simultaneous Multi-Threading (SMT) and fine-grained Interleaved Multi-Threading (IMT). The architecture has a modular configuration that can be fine-tuned at design time based on a target number of simultaneous threads and number of registers per execution unit, where execution unit resources are divided across logic used to execute multiple simultaneous threads. The number of logical threads that may be executed by the graphics execution unit 508 is not limited to the number of hardware threads, and multiple logical threads can be assigned to each hardware thread.

In one embodiment, the graphics execution unit 508 can co-issue multiple instructions, which may each be different instructions. The thread arbiter 522 of the graphics execution unit thread 508 can dispatch the instructions to one of the send unit 530, branch unit 532, or SIMD FPU(s) 534 for execution. Each execution thread can access 128 general-purpose registers within the GRF 524, where each register can store 32 bytes, accessible as a SIMD 8-element vector of 32-bit data elements. In one embodiment, each execution unit thread has access to 4 Kbytes within the GRF 524, although embodiments are not so limited, and greater or fewer register resources may be provided in other embodiments. In one embodiment the graphics execution unit 508 is partitioned into seven hardware threads that can independently perform computational operations, although the number of threads per execution unit can also vary according to embodiments. For example, in one embodiment up to 16 hardware threads are supported. In an embodiment in which seven threads may access 4 Kbytes, the GRF 524 can store a total of 28 Kbytes. Where 16 threads may access 4 Kbytes, the GRF 524 can store a total of 64 Kbytes. Flexible addressing modes can permit registers to be addressed together to build effectively wider registers or to represent strided rectangular block data structures.

In one embodiment, memory operations, sampler operations, and other longer-latency system communications are dispatched via “send” instructions that are executed by the message passing send unit 530. In one embodiment, branch instructions are dispatched to a dedicated branch unit 532 to facilitate SIMD divergence and eventual convergence.

In one embodiment the graphics execution unit 508 includes one or more SIMD floating point units (FPU(s)) 534 to perform floating-point operations. In one embodiment, the FPU(s) 534 also support integer computation. In one embodiment the FPU(s) 534 can SIMD execute up to M number of 32-bit floating-point (or integer) operations, or SIMD execute up to 2M 16-bit integer or 16-bit floating-point operations. In one embodiment, at least one of the FPU(s) provides extended math capability to support high-throughput transcendental math functions and double precision 54-bit floating-point. In some embodiments, a set of 8-bit integer SIMD ALUs 535 are also present, and may be specifically optimized to perform operations associated with machine learning computations.

In one embodiment, arrays of multiple instances of the graphics execution unit 508 can be instantiated in a graphics sub-core grouping (e.g., a sub-slice). For scalability, product architects can choose the exact number of execution units per sub-core grouping. In one embodiment the execution unit 508 can execute instructions across a plurality of execution channels. In a further embodiment, each thread executed on the graphics execution unit 508 is executed on a different channel.

FIG. 6 illustrates an additional execution unit 600, according to an embodiment. The execution unit 600 may be a compute-optimized execution unit for use in, for example, a compute engine tile 340A-340D as in FIG. 3C, but is not limited as such. Variants of the execution unit 600 may also be used in a graphics engine tile 310A-310D as in FIG. 3B. In one embodiment, the execution unit 600 includes a thread control unit 601, a thread state unit 602, an instruction fetch/prefetch unit 603, and an instruction decode unit 604. The execution unit 600 additionally includes a register file 606 that stores registers that can be assigned to hardware threads within the execution unit. The execution unit 600 additionally includes a send unit 607 and a branch unit 608. In one embodiment, the send unit 607 and branch unit 608 can operate similarly as the send unit 530 and a branch unit 532 of the graphics execution unit 508 of FIG. 5B.

The execution unit 600 also includes a compute unit 610 that includes multiple different types of functional units. In one embodiment the compute unit 610 includes an ALU unit 611 that includes an array of arithmetic logic units. The ALU unit 611 can be configured to perform 64-bit, 32-bit, and 16-bit integer and floating point operations. Integer and floating point operations may be performed simultaneously. The compute unit 610 can also include a systolic array 612, and a math unit 613. The systolic array 612 includes a W wide and D deep network of data processing units that can be used to perform vector or other data-parallel operations in a systolic manner. In one embodiment the systolic array 612 can be configured to perform matrix operations, such as matrix dot product operations. In one embodiment the systolic array 612 support 16-bit floating point operations, as well as 8-bit and 4-bit integer operations. In one embodiment the systolic array 612 can be configured to accelerate machine learning operations. In such embodiments, the systolic array 612 can be configured with support for the bfloat 16-bit floating point format. In one embodiment, a math unit 613 can be included to perform a specific subset of mathematical operations in an efficient and lower-power manner than then ALU unit 611. The math unit 613 can include a variant of math logic that may be found in shared function logic of a graphics processing engine provided by other embodiments (e.g., math logic 422 of the shared function logic 420 of FIG. 4). In one embodiment the math unit 613 can be configured to perform 32-bit and 64-bit floating point operations.

The thread control unit 601 includes logic to control the execution of threads within the execution unit. The thread control unit 601 can include thread arbitration logic to start, stop, and preempt execution of threads within the execution unit 600. The thread state unit 602 can be used to store thread state for threads assigned to execute on the execution unit 600. Storing the thread state within the execution unit 600 enables the rapid pre-emption of threads when those threads become blocked or idle. The instruction fetch/prefetch unit 603 can fetch instructions from an instruction cache of higher level execution logic (e.g., instruction cache 506 as in FIG. 5A). The instruction fetch/prefetch unit 603 can also issue prefetch requests for instructions to be loaded into the instruction cache based on an analysis of currently executing threads. The instruction decode unit 604 can be used to decode instructions to be executed by the compute units. In one embodiment, the instruction decode unit 604 can be used as a secondary decoder to decode complex instructions into constituent micro-operations.

The execution unit 600 additionally includes a register file 606 that can be used by hardware threads executing on the execution unit 600. Registers in the register file 606 can be divided across the logic used to execute multiple simultaneous threads within the compute unit 610 of the execution unit 600. The number of logical threads that may be executed by the graphics execution unit 600 is not limited to the number of hardware threads, and multiple logical threads can be assigned to each hardware thread. The size of the register file 606 can vary across embodiments based on the number of supported hardware threads. In one embodiment, register renaming may be used to dynamically allocate registers to hardware threads.

FIG. 7 is a block diagram illustrating graphics processor instruction formats 700 according to some embodiments. In one or more embodiment, the graphics processor execution units support an instruction set having instructions in multiple formats. The solid lined boxes illustrate the components that are generally included in an execution unit instruction, while the dashed lines include components that are optional or that are only included in a sub-set of the instructions. In some embodiments, instruction format 700 described and illustrated are macro-instructions, in that they are instructions supplied to the execution unit, as opposed to micro-operations resulting from instruction decode once the instruction is processed.

In some embodiments, the graphics processor execution units natively support instructions in a 128-bit instruction format 710. A 64-bit compacted instruction format 730 is available for some instructions based on the selected instruction, instruction options, and number of operands. The native 128-bit instruction format 710 provides access to all instruction options, while some options and operations are restricted in the 64-bit format 730. The native instructions available in the 64-bit format 730 vary by embodiment. In some embodiments, the instruction is compacted in part using a set of index values in an index field 713. The execution unit hardware references a set of compaction tables based on the index values and uses the compaction table outputs to reconstruct a native instruction in the 128-bit instruction format 710. Other sizes and formats of instruction can be used.

For each format, instruction opcode 712 defines the operation that the execution unit is to perform. The execution units execute each instruction in parallel across the multiple data elements of each operand. For example, in response to an add instruction the execution unit performs a simultaneous add operation across each color channel representing a texture element or picture element. By default, the execution unit performs each instruction across all data channels of the operands. In some embodiments, instruction control field 714 enables control over certain execution options, such as channels selection (e.g., predication) and data channel order (e.g., swizzle). For instructions in the 128-bit instruction format 710 an exec-size field 716 limits the number of data channels that will be executed in parallel. In some embodiments, exec-size field 716 is not available for use in the 64-bit compact instruction format 730.

Some execution unit instructions have up to three operands including two source operands, src0 720, src1 722, and one destination 718. In some embodiments, the execution units support dual destination instructions, where one of the destinations is implied. Data manipulation instructions can have a third source operand (e.g., SRC2 724), where the instruction opcode 712 determines the number of source operands. An instruction's last source operand can be an immediate (e.g., hard-coded) value passed with the instruction.

In some embodiments, the 128-bit instruction format 710 includes an access/address mode field 726 specifying, for example, whether direct register addressing mode or indirect register addressing mode is used. When direct register addressing mode is used, the register address of one or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 710 includes an access/address mode field 726, which specifies an address mode and/or an access mode for the instruction. In one embodiment the access mode is used to define a data access alignment for the instruction. Some embodiments support access modes including a 16-byte aligned access mode and a 1-byte aligned access mode, where the byte alignment of the access mode determines the access alignment of the instruction operands. For example, when in a first mode, the instruction may use byte-aligned addressing for source and destination operands and when in a second mode, the instruction may use 16-byte-aligned addressing for all source and destination operands.

In one embodiment, the address mode portion of the access/address mode field 726 determines whether the instruction is to use direct or indirect addressing. When direct register addressing mode is used bits in the instruction directly provide the register address of one or more operands. When indirect register addressing mode is used, the register address of one or more operands may be computed based on an address register value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 712 bit-fields to simplify Opcode decode 740. For an 8-bit opcode, bits 4, 5, and 6 allow the execution unit to determine the type of opcode. The precise opcode grouping shown is merely an example. In some embodiments, a move and logic opcode group 742 includes data movement and logic instructions (e.g., move (mov), compare (cmp)). In some embodiments, move and logic group 742 shares the five most significant bits (MSB), where move (mov) instructions are in the form of 0000xxxxb and logic instructions are in the form of 0001xxxxb. A flow control instruction group 744 (e.g., call, jump (jmp)) includes instructions in the form of 0010xxxxb (e.g., 0x20). A miscellaneous instruction group 746 includes a mix of instructions, including synchronization instructions (e.g., wait, send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instruction group 748 includes component-wise arithmetic instructions (e.g., add, multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel math group 748 performs the arithmetic operations in parallel across data channels. The vector math group 750 includes arithmetic instructions (e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math group performs arithmetic such as dot product calculations on vector operands. The illustrated opcode decode 740, in one embodiment, can be used to determine which portion of an execution unit will be used to execute a decoded instruction. For example, some instructions may be designated as systolic instructions that will be performed by a systolic array. Other instructions, such as ray-tracing instructions (not shown) can be routed to a ray-tracing core or ray-tracing logic within a slice or partition of execution logic.

Graphics Pipeline

FIG. 8 is a block diagram of another embodiment of a graphics processor 800. Elements of FIG. 8 having the same reference numbers (or names) as the elements of any other figure herein can operate or function in any manner similar to that described elsewhere herein, but are not limited to such.

In some embodiments, graphics processor 800 includes a geometry pipeline 820, a media pipeline 830, a display engine 840, thread execution logic 850, and a render output pipeline 870. In some embodiments, graphics processor 800 is a graphics processor within a multi-core processing system that includes one or more general-purpose processing cores. The graphics processor is controlled by register writes to one or more control registers (not shown) or via commands issued to graphics processor 800 via a ring interconnect 802. In some embodiments, ring interconnect 802 couples graphics processor 800 to other processing components, such as other graphics processors or general-purpose processors. Commands from ring interconnect 802 are interpreted by a command streamer 803, which supplies instructions to individual components of the geometry pipeline 820 or the media pipeline 830.

In some embodiments, command streamer 803 directs the operation of a vertex fetcher 805 that reads vertex data from memory and executes vertex-processing commands provided by command streamer 803. In some embodiments, vertex fetcher 805 provides vertex data to a vertex shader 807, which performs coordinate space transformation and lighting operations to each vertex. In some embodiments, vertex fetcher 805 and vertex shader 807 execute vertex-processing instructions by dispatching execution threads to execution units 852A-852B via a thread dispatcher 831.

In some embodiments, execution units 852A-852B are an array of vector processors having an instruction set for performing graphics and media operations. In some embodiments, execution units 852A-852B have an attached L1 cache 851 that is specific for each array or shared between the arrays. The cache can be configured as a data cache, an instruction cache, or a single cache that is partitioned to contain data and instructions in different partitions.

In some embodiments, geometry pipeline 820 includes tessellation components to perform hardware-accelerated tessellation of 3D objects. In some embodiments, a programmable hull shader 811 configures the tessellation operations. A programmable domain shader 817 provides back-end evaluation of tessellation output. A tessellator 813 operates at the direction of hull shader 811 and contains special purpose logic to generate a set of detailed geometric objects based on a coarse geometric model that is provided as input to geometry pipeline 820. In some embodiments, if tessellation is not used, tessellation components (e.g., hull shader 811, tessellator 813, and domain shader 817) can be bypassed.

In some embodiments, complete geometric objects can be processed by a geometry shader 819 via one or more threads dispatched to execution units 852A-852B, or can proceed directly to the clipper 829. In some embodiments, the geometry shader operates on entire geometric objects, rather than vertices or patches of vertices as in previous stages of the graphics pipeline. If the tessellation is disabled the geometry shader 819 receives input from the vertex shader 807. In some embodiments, geometry shader 819 is programmable by a geometry shader program to perform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 829 processes vertex data. The clipper 829 may be a fixed function clipper or a programmable clipper having clipping and geometry shader functions. In some embodiments, a rasterizer and depth test component 873 in the render output pipeline 870 dispatches pixel shaders to convert the geometric objects into per pixel representations. In some embodiments, pixel shader logic is included in thread execution logic 850. In some embodiments, an application can bypass the rasterizer and depth test component 873 and access un-rasterized vertex data via a stream out unit 823.

The graphics processor 800 has an interconnect bus, interconnect fabric, or some other interconnect mechanism that allows data and message passing amongst the major components of the processor. In some embodiments, execution units 852A-852B and associated logic units (e.g., L1 cache 851, sampler 854, texture cache 858, etc.) interconnect via a data port 856 to perform memory access and communicate with render output pipeline components of the processor. In some embodiments, sampler 854, caches 851, 858 and execution units 852A-852B each have separate memory access paths. In one embodiment the texture cache 858 can also be configured as a sampler cache.

In some embodiments, render output pipeline 870 contains a rasterizer and depth test component 873 that converts vertex-based objects into an associated pixel-based representation. In some embodiments, the rasterizer logic includes a windower/masker unit to perform fixed function triangle and line rasterization. An associated render cache 878 and depth cache 879 are also available in some embodiments. A pixel operations component 877 performs pixel-based operations on the data, though in some instances, pixel operations associated with 2D operations (e.g. bit block image transfers with blending) are performed by the 2D engine 841, or substituted at display time by the display controller 843 using overlay display planes. In some embodiments, a shared L3 cache 875 is available to all graphics components, allowing the sharing of data without the use of main system memory.

In some embodiments, graphics processor media pipeline 830 includes a media engine 837 and a video front-end 834. In some embodiments, video front-end 834 receives pipeline commands from the command streamer 803. In some embodiments, media pipeline 830 includes a separate command streamer. In some embodiments, video front-end 834 processes media commands before sending the command to the media engine 837. In some embodiments, media engine 837 includes thread spawning functionality to spawn threads for dispatch to thread execution logic 850 via thread dispatcher 831.

In some embodiments, graphics processor 800 includes a display engine 840. In some embodiments, display engine 840 is external to processor 800 and couples with the graphics processor via the ring interconnect 802, or some other interconnect bus or fabric. In some embodiments, display engine 840 includes a 2D engine 841 and a display controller 843. In some embodiments, display engine 840 contains special purpose logic capable of operating independently of the 3D pipeline. In some embodiments, display controller 843 couples with a display device (not shown), which may be a system integrated display device, as in a laptop computer, or an external display device attached via a display device connector.

In some embodiments, the geometry pipeline 820 and media pipeline 830 are configurable to perform operations based on multiple graphics and media programming interfaces and are not specific to any one application programming interface (API). In some embodiments, driver software for the graphics processor translates API calls that are specific to a particular graphics or media library into commands that can be processed by the graphics processor. In some embodiments, support is provided for the Open Graphics Library (OpenGL), Open Computing Language (OpenCL), and/or Vulkan graphics and compute API, all from the Khronos Group. In some embodiments, support may also be provided for the Direct3D library from the Microsoft Corporation. In some embodiments, a combination of these libraries may be supported. Support may also be provided for the Open Source Computer Vision Library (OpenCV). A future API with a compatible 3D pipeline would also be supported if a mapping can be made from the pipeline of the future API to the pipeline of the graphics processor.

Graphics Pipeline Programming

FIG. 9A is a block diagram illustrating a graphics processor command format 900 according to some embodiments. FIG. 9B is a block diagram illustrating a graphics processor command sequence 910 according to an embodiment. The solid lined boxes in FIG. 9A illustrate the components that are generally included in a graphics command while the dashed lines include components that are optional or that are only included in a sub-set of the graphics commands. The exemplary graphics processor command format 900 of FIG. 9A includes data fields to identify a client 902, a command operation code (opcode) 904, and data 906 for the command. A sub-opcode 905 and a command size 908 are also included in some commands.

In some embodiments, client 902 specifies the client unit of the graphics device that processes the command data. In some embodiments, a graphics processor command parser examines the client field of each command to condition the further processing of the command and route the command data to the appropriate client unit. In some embodiments, the graphics processor client units include a memory interface unit, a render unit, a 2D unit, a 3D unit, and a media unit. Each client unit has a corresponding processing pipeline that processes the commands. Once the command is received by the client unit, the client unit reads the opcode 904 and, if present, sub-opcode 905 to determine the operation to perform. The client unit performs the command using information in data field 906. For some commands an explicit command size 908 is expected to specify the size of the command. In some embodiments, the command parser automatically determines the size of at least some of the commands based on the command opcode. In some embodiments commands are aligned via multiples of a double word. Other command formats can be used.

The flow diagram in FIG. 9B illustrates an exemplary graphics processor command sequence 910. In some embodiments, software or firmware of a data processing system that features an embodiment of a graphics processor uses a version of the command sequence shown to set up, execute, and terminate a set of graphics operations. A sample command sequence is shown and described for purposes of example only as embodiments are not limited to these specific commands or to this command sequence. Moreover, the commands may be issued as batch of commands in a command sequence, such that the graphics processor will process the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 may begin with a pipeline flush command 912 to cause any active graphics pipeline to complete the currently pending commands for the pipeline. In some embodiments, the 3D pipeline 922 and the media pipeline 924 do not operate concurrently. The pipeline flush is performed to cause the active graphics pipeline to complete any pending commands. In response to a pipeline flush, the command parser for the graphics processor will pause command processing until the active drawing engines complete pending operations and the relevant read caches are invalidated. Optionally, any data in the render cache that is marked ‘dirty’ can be flushed to memory. In some embodiments, pipeline flush command 912 can be used for pipeline synchronization or before placing the graphics processor into a low power state.

In some embodiments, a pipeline select command 913 is used when a command sequence requires the graphics processor to explicitly switch between pipelines. In some embodiments, a pipeline select command 913 is required only once within an execution context before issuing pipeline commands unless the context is to issue commands for both pipelines. In some embodiments, a pipeline flush command 912 is required immediately before a pipeline switch via the pipeline select command 913.

In some embodiments, a pipeline control command 914 configures a graphics pipeline for operation and is used to program the 3D pipeline 922 and the media pipeline 924. In some embodiments, pipeline control command 914 configures the pipeline state for the active pipeline. In one embodiment, the pipeline control command 914 is used for pipeline synchronization and to clear data from one or more cache memories within the active pipeline before processing a batch of commands.

In some embodiments, return buffer state commands 916 are used to configure a set of return buffers for the respective pipelines to write data. Some pipeline operations require the allocation, selection, or configuration of one or more return buffers into which the operations write intermediate data during processing. In some embodiments, the graphics processor also uses one or more return buffers to store output data and to perform cross thread communication. In some embodiments, the return buffer state 916 includes selecting the size and number of return buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on the active pipeline for operations. Based on a pipeline determination 920, the command sequence is tailored to the 3D pipeline 922 beginning with the 3D pipeline state 930 or the media pipeline 924 beginning at the media pipeline state 940.

The commands to configure the 3D pipeline state 930 include 3D state setting commands for vertex buffer state, vertex element state, constant color state, depth buffer state, and other state variables that are to be configured before 3D primitive commands are processed. The values of these commands are determined at least in part based on the particular 3D API in use. In some embodiments, 3D pipeline state 930 commands are also able to selectively disable or bypass certain pipeline elements if those elements will not be used.

In some embodiments, 3D primitive 932 command is used to submit 3D primitives to be processed by the 3D pipeline. Commands and associated parameters that are passed to the graphics processor via the 3D primitive 932 command are forwarded to the vertex fetch function in the graphics pipeline. The vertex fetch function uses the 3D primitive 932 command data to generate vertex data structures. The vertex data structures are stored in one or more return buffers. In some embodiments, 3D primitive 932 command is used to perform vertex operations on 3D primitives via vertex shaders. To process vertex shaders, 3D pipeline 922 dispatches shader execution threads to graphics processor execution units.

In some embodiments, 3D pipeline 922 is triggered via an execute 934 command or event. In some embodiments, a register write triggers command execution. In some embodiments execution is triggered via a ‘go’ or ‘kick’ command in the command sequence. In one embodiment, command execution is triggered using a pipeline synchronization command to flush the command sequence through the graphics pipeline. The 3D pipeline will perform geometry processing for the 3D primitives. Once operations are complete, the resulting geometric objects are rasterized and the pixel engine colors the resulting pixels. Additional commands to control pixel shading and pixel back end operations may also be included for those operations.

In some embodiments, the graphics processor command sequence 910 follows the media pipeline 924 path when performing media operations. In general, the specific use and manner of programming for the media pipeline 924 depends on the media or compute operations to be performed. Specific media decode operations may be offloaded to the media pipeline during media decode. In some embodiments, the media pipeline can also be bypassed and media decode can be performed in whole or in part using resources provided by one or more general-purpose processing cores. In one embodiment, the media pipeline also includes elements for general-purpose graphics processor unit (GPGPU) operations, where the graphics processor is used to perform SIMD vector operations using computational shader programs that are not explicitly related to the rendering of graphics primitives.

In some embodiments, media pipeline 924 is configured in a similar manner as the 3D pipeline 922. A set of commands to configure the media pipeline state 940 are dispatched or placed into a command queue before the media object commands 942. In some embodiments, commands for the media pipeline state 940 include data to configure the media pipeline elements that will be used to process the media objects. This includes data to configure the video decode and video encode logic within the media pipeline, such as encode or decode format. In some embodiments, commands for the media pipeline state 940 also support the use of one or more pointers to “indirect” state elements that contain a batch of state settings.

In some embodiments, media object commands 942 supply pointers to media objects for processing by the media pipeline. The media objects include memory buffers containing video data to be processed. In some embodiments, all media pipeline states must be valid before issuing a media object command 942. Once the pipeline state is configured and media object commands 942 are queued, the media pipeline 924 is triggered via an execute command 944 or an equivalent execute event (e.g., register write). Output from media pipeline 924 may then be post processed by operations provided by the 3D pipeline 922 or the media pipeline 924. In some embodiments, GPGPU operations are configured and executed in a similar manner as media operations.

Graphics Software Architecture

FIG. 10 illustrates an exemplary graphics software architecture for a data processing system 1000 according to some embodiments. In some embodiments, software architecture includes a 3D graphics application 1010, an operating system 1020, and at least one processor 1030. In some embodiments, processor 1030 includes a graphics processor 1032 and one or more general-purpose processor core(s) 1034. The graphics application 1010 and operating system 1020 each execute in the system memory 1050 of the data processing system.

In some embodiments, 3D graphics application 1010 contains one or more shader programs including shader instructions 1012. The shader language instructions may be in a high-level shader language, such as the High-Level Shader Language (HLSL) of Direct3D, the OpenGL Shader Language (GLSL), and so forth. The application also includes executable instructions 1014 in a machine language suitable for execution by the general-purpose processor core 1034. The application also includes graphics objects 1016 defined by vertex data.

In some embodiments, operating system 1020 is a Microsoft® Windows® operating system from the Microsoft Corporation, a proprietary UNIX-like operating system, or an open source UNIX-like operating system using a variant of the Linux kernel. The operating system 1020 can support a graphics API 1022 such as the Direct3D API, the OpenGL API, or the Vulkan API. When the Direct3D API is in use, the operating system 1020 uses a front-end shader compiler 1024 to compile any shader instructions 1012 in HLSL into a lower-level shader language. The compilation may be a just-in-time (JIT) compilation or the application can perform shader pre-compilation. In some embodiments, high-level shaders are compiled into low-level shaders during the compilation of the 3D graphics application 1010. In some embodiments, the shader instructions 1012 are provided in an intermediate form, such as a version of the Standard Portable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 1026 contains a back-end shader compiler 1027 to convert the shader instructions 1012 into a hardware specific representation. When the OpenGL API is in use, shader instructions 1012 in the GLSL high-level language are passed to a user mode graphics driver 1026 for compilation. In some embodiments, user mode graphics driver 1026 uses operating system kernel mode functions 1028 to communicate with a kernel mode graphics driver 1029. In some embodiments, kernel mode graphics driver 1029 communicates with graphics processor 1032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented by representative code stored on a machine-readable medium which represents and/or defines logic within an integrated circuit such as a processor. For example, the machine-readable medium may include instructions which represent various logic within the processor. When read by a machine, the instructions may cause the machine to fabricate the logic to perform the techniques described herein. Such representations, known as “IP cores,” are reusable units of logic for an integrated circuit that may be stored on a tangible, machine-readable medium as a hardware model that describes the structure of the integrated circuit. The hardware model may be supplied to various customers or manufacturing facilities, which load the hardware model on fabrication machines that manufacture the integrated circuit. The integrated circuit may be fabricated such that the circuit performs operations described in association with any of the embodiments described herein.

FIG. 11A is a block diagram illustrating an IP core development system 1100 that may be used to manufacture an integrated circuit to perform operations according to an embodiment. The IP core development system 1100 may be used to generate modular, re-usable designs that can be incorporated into a larger design or used to construct an entire integrated circuit (e.g., an SOC integrated circuit). A design facility 1130 can generate a software simulation 1110 of an IP core design in a high-level programming language (e.g., C/C++). The software simulation 1110 can be used to design, test, and verify the behavior of the IP core using a simulation model 1112. The simulation model 1112 may include functional, behavioral, and/or timing simulations. A register transfer level (RTL) design 1115 can then be created or synthesized from the simulation model 1112. The RTL design 1115 is an abstraction of the behavior of the integrated circuit that models the flow of digital signals between hardware registers, including the associated logic performed using the modeled digital signals. In addition to an RTL design 1115, lower-level designs at the logic level or transistor level may also be created, designed, or synthesized. Thus, the particular details of the initial design and simulation may vary.

The RTL design 1115 or equivalent may be further synthesized by the design facility into a hardware model 1120, which may be in a hardware description language (HDL), or some other representation of physical design data. The HDL may be further simulated or tested to verify the IP core design. The IP core design can be stored for delivery to a 3^(rd) party fabrication facility 1165 using non-volatile memory 1140 (e.g., hard disk, flash memory, or any non-volatile storage medium). Alternatively, the IP core design may be transmitted (e.g., via the Internet) over a wired connection 1150 or wireless connection 1160. The fabrication facility 1165 may then fabricate an integrated circuit that is based at least in part on the IP core design. The fabricated integrated circuit can be configured to perform operations in accordance with at least one embodiment described herein.

FIG. 11B illustrates a cross-section side view of an integrated circuit package assembly 1170, according to some embodiments described herein. The integrated circuit package assembly 1170 illustrates an implementation of one or more processor or accelerator devices as described herein. The package assembly 1170 includes multiple units of hardware logic 1172, 1174 connected to a substrate 1180. The logic 1172, 1174 may be implemented at least partly in configurable logic or fixed-functionality logic hardware, and can include one or more portions of any of the processor core(s), graphics processor(s), or other accelerator devices described herein. Each unit of logic 1172, 1174 can be implemented within a semiconductor die and coupled with the substrate 1180 via an interconnect structure 1173. The interconnect structure 1173 may be configured to route electrical signals between the logic 1172, 1174 and the substrate 1180, and can include interconnects such as, but not limited to bumps or pillars. In some embodiments, the interconnect structure 1173 may be configured to route electrical signals such as, for example, input/output (I/O) signals and/or power or ground signals associated with the operation of the logic 1172, 1174. In some embodiments, the substrate 1180 is an epoxy-based laminate substrate. The substrate 1180 may include other suitable types of substrates in other embodiments. The package assembly 1170 can be connected to other electrical devices via a package interconnect 1183. The package interconnect 1183 may be coupled to a surface of the substrate 1180 to route electrical signals to other electrical devices, such as a motherboard, other chipset, or multi-chip module.

In some embodiments, the units of logic 1172, 1174 are electrically coupled with a bridge 1182 that is configured to route electrical signals between the logic 1172, 1174. The bridge 1182 may be a dense interconnect structure that provides a route for electrical signals. The bridge 1182 may include a bridge substrate composed of glass or a suitable semiconductor material. Electrical routing features can be formed on the bridge substrate to provide a chip-to-chip connection between the logic 1172, 1174.

Although two units of logic 1172, 1174 and a bridge 1182 are illustrated, embodiments described herein may include more or fewer logic units on one or more dies. The one or more dies may be connected by zero or more bridges, as the bridge 1182 may be excluded when the logic is included on a single die. Alternatively, multiple dies or units of logic can be connected by one or more bridges. Additionally, multiple logic units, dies, and bridges can be connected together in other possible configurations, including three-dimensional configurations.

FIG. 11C illustrates a package assembly 1190 that includes multiple units of hardware logic chiplets connected to a substrate 1180 (e.g., base die). A graphics processing unit, parallel processor, and/or compute accelerator as described herein can be composed from diverse silicon chiplets that are separately manufactured. In this context, a chiplet is an at least partially packaged integrated circuit that includes distinct units of logic that can be assembled with other chiplets into a larger package. A diverse set of chiplets with different IP core logic can be assembled into a single device. Additionally the chiplets can be integrated into a base die or base chiplet using active interposer technology. The concepts described herein enable the interconnection and communication between the different forms of IP within the GPU. IP cores can be manufactured using different process technologies and composed during manufacturing, which avoids the complexity of converging multiple IPs, especially on a large SoC with several flavors IPs, to the same manufacturing process. Enabling the use of multiple process technologies improves the time to market and provides a cost-effective way to create multiple product SKUs. Additionally, the disaggregated IPs are more amenable to being power gated independently, components that are not in use on a given workload can be powered off, reducing overall power consumption.

The hardware logic chiplets can include special purpose hardware logic chiplets 1172, logic or I/O chiplets 1174, and/or memory chiplets 1175. The hardware logic chiplets 1172 and logic or I/O chiplets 1174 may be implemented at least partly in configurable logic or fixed-functionality logic hardware and can include one or more portions of any of the processor core(s), graphics processor(s), parallel processors, or other accelerator devices described herein. The memory chiplets 1175 can be DRAM (e.g., GDDR, HBM) memory or cache (SRAM) memory.

Each chiplet can be fabricated as separate semiconductor die and coupled with the substrate 1180 via an interconnect structure 1173. The interconnect structure 1173 may be configured to route electrical signals between the various chiplets and logic within the substrate 1180. The interconnect structure 1173 can include interconnects such as, but not limited to bumps or pillars. In some embodiments, the interconnect structure 1173 may be configured to route electrical signals such as, for example, input/output (I/O) signals and/or power or ground signals associated with the operation of the logic, I/O and memory chiplets.

In some embodiments, the substrate 1180 is an epoxy-based laminate substrate. The substrate 1180 may include other suitable types of substrates in other embodiments. The package assembly 1190 can be connected to other electrical devices via a package interconnect 1183. The package interconnect 1183 may be coupled to a surface of the substrate 1180 to route electrical signals to other electrical devices, such as a motherboard, other chipset, or multi-chip module.

In some embodiments, a logic or I/O chiplet 1174 and a memory chiplet 1175 can be electrically coupled via a bridge 1187 that is configured to route electrical signals between the logic or I/O chiplet 1174 and a memory chiplet 1175. The bridge 1187 may be a dense interconnect structure that provides a route for electrical signals. The bridge 1187 may include a bridge substrate composed of glass or a suitable semiconductor material. Electrical routing features can be formed on the bridge substrate to provide a chip-to-chip connection between the logic or I/O chiplet 1174 and a memory chiplet 1175. The bridge 1187 may also be referred to as a silicon bridge or an interconnect bridge. For example, the bridge 1187, in some embodiments, is an Embedded Multi-die Interconnect Bridge (EMIB). In some embodiments, the bridge 1187 may simply be a direct connection from one chiplet to another chiplet.

The substrate 1180 can include hardware components for I/O 1191, cache memory 1192, and other hardware logic 1193. A fabric 1185 can be embedded in the substrate 1180 to enable communication between the various logic chiplets and the logic 1191, 1193 within the substrate 1180. In one embodiment, the I/O 1191, fabric 1185, cache, bridge, and other hardware logic 1193 can be integrated into a base die that is layered on top of the substrate 1180.

In various embodiments a package assembly 1190 can include fewer or greater number of components and chiplets that are interconnected by a fabric 1185 or one or more bridges 1187. The chiplets within the package assembly 1190 may be arranged in a 3D or 2.5D arrangement. In general, bridge structures 1187 may be used to facilitate a point to point interconnect between, for example, logic or I/O chiplets and memory chiplets. The fabric 1185 can be used to interconnect the various logic and/or I/O chiplets (e.g., chiplets 1172, 1174, 1191, 1193). with other logic and/or I/O chiplets. In one embodiment, the cache memory 1192 within the substrate can act as a global cache for the package assembly 1190, part of a distributed global cache, or as a dedicated cache for the fabric 1185.

FIG. 11D illustrates a package assembly 1194 including interchangeable chiplets 1195, according to an embodiment. The interchangeable chiplets 1195 can be assembled into standardized slots on one or more base chiplets 1196, 1198. The base chiplets 1196, 1198 can be coupled via a bridge interconnect 1197, which can be similar to the other bridge interconnects described herein and may be, for example, an EMIB. Memory chiplets can also be connected to logic or I/O chiplets via a bridge interconnect. I/O and logic chiplets can communicate via an interconnect fabric. The base chiplets can each support one or more slots in a standardized format for one of logic or I/O or memory/cache.

In one embodiment, SRAM and power delivery circuits can be fabricated into one or more of the base chiplets 1196, 1198, which can be fabricated using a different process technology relative to the interchangeable chiplets 1195 that are stacked on top of the base chiplets. For example, the base chiplets 1196, 1198 can be fabricated using a larger process technology, while the interchangeable chiplets can be manufactured using a smaller process technology. One or more of the interchangeable chiplets 1195 may be memory (e.g., DRAM) chiplets. Different memory densities can be selected for the package assembly 1194 based on the power, and/or performance targeted for the product that uses the package assembly 1194. Additionally, logic chiplets with a different number of type of functional units can be selected at time of assembly based on the power, and/or performance targeted for the product. Additionally, chiplets containing IP logic cores of differing types can be inserted into the interchangeable chiplet slots, enabling hybrid processor designs that can mix and match different technology IP blocks.

Exemplary System on a Chip Integrated Circuit

FIGS. 12-13 illustrate exemplary integrated circuits and associated graphics processors that may be fabricated using one or more IP cores, according to various embodiments described herein. In addition to what is illustrated, other logic and circuits may be included, including additional graphics processors/cores, peripheral interface controllers, or general-purpose processor cores.

FIG. 12 is a block diagram illustrating an exemplary system on a chip integrated circuit 1200 that may be fabricated using one or more IP cores, according to an embodiment. Exemplary integrated circuit 1200 includes one or more application processor(s) 1205 (e.g., CPUs), at least one graphics processor 1210, and may additionally include an image processor 1215 and/or a video processor 1220, any of which may be a modular IP core from the same or multiple different design facilities. Integrated circuit 1200 includes peripheral or bus logic including a USB controller 1225, UART controller 1230, an SPI/SDIO controller 1235, and an I²S/I²C controller 1240. Additionally, the integrated circuit can include a display device 1245 coupled to one or more of a high-definition multimedia interface (HDMI) controller 1250 and a mobile industry processor interface (MIPI) display interface 1255. Storage may be provided by a flash memory subsystem 1260 including flash memory and a flash memory controller. Memory interface may be provided via a memory controller 1265 for access to SDRAM or SRAM memory devices. Some integrated circuits additionally include an embedded security engine 1270.

FIGS. 13A-13B are block diagrams illustrating exemplary graphics processors for use within an SoC, according to embodiments described herein. FIG. 13A illustrates an exemplary graphics processor 1310 of a system on a chip integrated circuit that may be fabricated using one or more IP cores, according to an embodiment. FIG. 13B illustrates an additional exemplary graphics processor 1340 of a system on a chip integrated circuit that may be fabricated using one or more IP cores, according to an embodiment. Graphics processor 1310 of FIG. 13A is an example of a low power graphics processor core. Graphics processor 1340 of FIG. 13B is an example of a higher performance graphics processor core. Each of the graphics processors 1310, 1340 can be variants of the graphics processor 1210 of FIG. 12.

As shown in FIG. 13A, graphics processor 1310 includes a vertex processor 1305 and one or more fragment processor(s) 1315A-1315N (e.g., 1315A, 1315B, 1315C, 1315D, through 1315N-1, and 1315N). Graphics processor 1310 can execute different shader programs via separate logic, such that the vertex processor 1305 is optimized to execute operations for vertex shader programs, while the one or more fragment processor(s) 1315A-1315N execute fragment (e.g., pixel) shading operations for fragment or pixel shader programs. The vertex processor 1305 performs the vertex processing stage of the 3D graphics pipeline and generates primitives and vertex data. The fragment processor(s) 1315A-1315N use the primitive and vertex data generated by the vertex processor 1305 to produce a framebuffer that is displayed on a display device. In one embodiment, the fragment processor(s) 1315A-1315N are optimized to execute fragment shader programs as provided for in the OpenGL API, which may be used to perform similar operations as a pixel shader program as provided for in the Direct 3D API.

Graphics processor 1310 additionally includes one or more memory management units (MMUs) 1320A-1320B, cache(s) 1325A-1325B, and circuit interconnect(s) 1330A-1330B. The one or more MMU(s) 1320A-1320B provide for virtual to physical address mapping for the graphics processor 1310, including for the vertex processor 1305 and/or fragment processor(s) 1315A-1315N, which may reference vertex or image/texture data stored in memory, in addition to vertex or image/texture data stored in the one or more cache(s) 1325A-1325B. In one embodiment the one or more MMU(s) 1320A-1320B may be synchronized with other MMUs within the system, including one or more MMUs associated with the one or more application processor(s) 1205, image processor 1215, and/or video processor 1220 of FIG. 12, such that each processor 1205-1220 can participate in a shared or unified virtual memory system. The one or more circuit interconnect(s) 1330A-1330B enable graphics processor 1310 to interface with other IP cores within the SoC, either via an internal bus of the SoC or via a direct connection, according to embodiments.

As shown FIG. 13B, graphics processor 1340 includes the one or more MMU(s) 1320A-1320B, cache(s) 1325A-1325B, and circuit interconnect(s) 1330A-1330B of the graphics processor 1310 of FIG. 13A. Graphics processor 1340 includes one or more shader core(s) 1355A-1355N (e.g., 1455A, 1355B, 1355C, 1355D, 1355E, 1355F, through 1355N-1, and 1355N), which provides for a unified shader core architecture in which a single core or type or core can execute all types of programmable shader code, including shader program code to implement vertex shaders, fragment shaders, and/or compute shaders. The exact number of shader cores present can vary among embodiments and implementations. Additionally, graphics processor 1340 includes an inter-core task manager 1345, which acts as a thread dispatcher to dispatch execution threads to one or more shader cores 1355A-1355N and a tiling unit 1358 to accelerate tiling operations for tile-based rendering, in which rendering operations for a scene are subdivided in image space, for example to exploit local spatial coherence within a scene or to optimize use of internal caches.

Machine Learning Overview

A machine learning algorithm is an algorithm that can learn based on a set of data. Embodiments of machine learning algorithms can be designed to model high-level abstractions within a data set. For example, image recognition algorithms can be used to determine which of several categories to which a given input belong; regression algorithms can output a numerical value given an input; and pattern recognition algorithms can be used to generate translated text or perform text to speech and/or speech recognition.

An exemplary type of machine learning algorithm is a neural network. There are many types of neural networks; a simple type of neural network is a feedforward network. A feedforward network may be implemented as an acyclic graph in which the nodes are arranged in layers. Typically, a feedforward network topology includes an input layer and an output layer that are separated by at least one hidden layer. The hidden layer transforms input received by the input layer into a representation that is useful for generating output in the output layer. The network nodes are fully connected via edges to the nodes in adjacent layers, but there are no edges between nodes within each layer. Data received at the nodes of an input layer of a feedforward network are propagated (i.e., “fed forward”) to the nodes of the output layer via an activation function that calculates the states of the nodes of each successive layer in the network based on coefficients (“weights”) respectively associated with each of the edges connecting the layers. Depending on the specific model being represented by the algorithm being executed, the output from the neural network algorithm can take various forms.

Before a machine learning algorithm can be used to model a particular problem, the algorithm is trained using a training data set. Training a neural network involves selecting a network topology, using a set of training data representing a problem being modeled by the network, and adjusting the weights until the network model performs with a minimal error for all instances of the training data set. For example, during a supervised learning training process for a neural network, the output produced by the network in response to the input representing an instance in a training data set is compared to the “correct” labeled output for that instance, an error signal representing the difference between the output and the labeled output is calculated, and the weights associated with the connections are adjusted to minimize that error as the error signal is backward propagated through the layers of the network. The network is considered “trained” when the errors for each of the outputs generated from the instances of the training data set are minimized.

The accuracy of a machine learning algorithm can be affected significantly by the quality of the data set used to train the algorithm. The training process can be computationally intensive and may require a significant amount of time on a conventional general-purpose processor. Accordingly, parallel processing hardware is used to train many types of machine learning algorithms. This is particularly useful for optimizing the training of neural networks, as the computations performed in adjusting the coefficients in neural networks lend themselves naturally to parallel implementations. Specifically, many machine learning algorithms and software applications have been adapted to make use of the parallel processing hardware within general-purpose graphics processing devices.

FIG. 14 is a generalized diagram of a machine learning software stack 1400. A machine learning application 1402 can be configured to train a neural network using a training dataset or to use a trained deep neural network to implement machine intelligence. The machine learning application 1402 can include training and inference functionality for a neural network and/or specialized software that can be used to train a neural network before deployment. The machine learning application 1402 can implement any type of machine intelligence including but not limited to image recognition, mapping and localization, autonomous navigation, speech synthesis, medical imaging, or language translation.

Hardware acceleration for the machine learning application 1402 can be enabled via a machine learning framework 1404. The machine learning framework 1404 can provide a library of machine learning primitives. Machine learning primitives are basic operations that are commonly performed by machine learning algorithms. Without the machine learning framework 1404, developers of machine learning algorithms would be required to create and optimize the main computational logic associated with the machine learning algorithm, then re-optimize the computational logic as new parallel processors are developed. Instead, the machine learning application can be configured to perform the necessary computations using the primitives provided by the machine learning framework 1404. Exemplary primitives include tensor convolutions, activation functions, and pooling, which are computational operations that are performed while training a convolutional neural network (CNN). The machine learning framework 1404 can also provide primitives to implement basic linear algebra subprograms performed by many machine-learning algorithms, such as matrix and vector operations.

The machine learning framework 1404 can process input data received from the machine learning application 1402 and generate the appropriate input to a compute framework 1406. The compute framework 1406 can abstract the underlying instructions provided to the GPGPU driver 1408 to enable the machine learning framework 1404 to take advantage of hardware acceleration via the GPGPU hardware 1410 without requiring the machine learning framework 1404 to have intimate knowledge of the architecture of the GPGPU hardware 1410. Additionally, the compute framework 1406 can enable hardware acceleration for the machine learning framework 1404 across a variety of types and generations of the GPGPU hardware 1410.

Machine Learning Neural Network Implementations

The computing architecture provided by embodiments described herein can be configured to perform the types of parallel processing that is particularly suited for training and deploying neural networks for machine learning. A neural network can be generalized as a network of functions having a graph relationship. As is known in the art, there are a variety of types of neural network implementations used in machine learning. One exemplary type of neural network is the feedforward network, as previously described.

A second exemplary type of neural network is the Convolutional Neural Network (CNN). A CNN is a specialized feedforward neural network for processing data having a known, grid-like topology, such as image data. Accordingly, CNNs are commonly used for computer vision and image recognition applications, but they also may be used for other types of pattern recognition such as speech and language processing. The nodes in the CNN input layer are organized into a set of “filters” (feature detectors inspired by the receptive fields found in the retina), and the output of each set of filters is propagated to nodes in successive layers of the network. The computations for a CNN include applying the convolution mathematical operation to each filter to produce the output of that filter. Convolution is a specialized kind of mathematical operation performed by two functions to produce a third function that is a modified version of one of the two original functions. In convolutional network terminology, the first function to the convolution can be referred to as the input, while the second function can be referred to as the convolution kernel. The output may be referred to as the feature map. For example, the input to a convolution layer can be a multidimensional array of data that defines the various color components of an input image. The convolution kernel can be a multidimensional array of parameters, where the parameters are adapted by the training process for the neural network.

Recurrent neural networks (RNNs) are a family of feedforward neural networks that include feedback connections between layers. RNNs enable modeling of sequential data by sharing parameter data across different parts of the neural network. The architecture for a RNN includes cycles. The cycles represent the influence of a present value of a variable on its own value at a future time, as at least a portion of the output data from the RNN is used as feedback for processing subsequent input in a sequence. This feature makes RNNs particularly useful for language processing due to the variable nature in which language data can be composed.

The figures described below present exemplary feedforward, CNN, and RNN networks, as well as describe a general process for respectively training and deploying each of those types of networks. It will be understood that these descriptions are exemplary and non-limiting as to any specific embodiment described herein and the concepts illustrated can be applied generally to deep neural networks and machine learning techniques in general.

The exemplary neural networks described above can be used to perform deep learning. Deep learning is machine learning using deep neural networks. The deep neural networks used in deep learning are artificial neural networks composed of multiple hidden layers, as opposed to shallow neural networks that include only a single hidden layer. Deeper neural networks are generally more computationally intensive to train. However, the additional hidden layers of the network enable multistep pattern recognition that results in reduced output error relative to shallow machine learning techniques.

Deep neural networks used in deep learning typically include a front-end network to perform feature recognition coupled to a back-end network which represents a mathematical model that can perform operations (e.g., object classification, speech recognition, etc.) based on the feature representation provided to the model. Deep learning enables machine learning to be performed without requiring hand crafted feature engineering to be performed for the model. Instead, deep neural networks can learn features based on statistical structure or correlation within the input data. The learned features can be provided to a mathematical model that can map detected features to an output. The mathematical model used by the network is generally specialized for the specific task to be performed, and different models will be used to perform different task.

Once the neural network is structured, a learning model can be applied to the network to train the network to perform specific tasks. The learning model describes how to adjust the weights within the model to reduce the output error of the network. Backpropagation of errors is a common method used to train neural networks. An input vector is presented to the network for processing. The output of the network is compared to the desired output using a loss function and an error value is calculated for each of the neurons in the output layer. The error values are then propagated backwards until each neuron has an associated error value which roughly represents its contribution to the original output. The network can then learn from those errors using an algorithm, such as the stochastic gradient descent algorithm, to update the weights of the of the neural network.

FIG. 15A-15B illustrate an exemplary convolutional neural network. FIG. 15A illustrates various layers within a CNN. As shown in FIG. 15A, an exemplary CNN used to model image processing can receive input 1502 describing the red, green, and blue (RGB) components of an input image. The input 1502 can be processed by multiple convolutional layers (e.g., first convolutional layer 1504, second convolutional layer 1506). The output from the multiple convolutional layers may optionally be processed by a set of fully connected layers 1508. Neurons in a fully connected layer have full connections to all activations in the previous layer, as previously described for a feedforward network. The output from the fully connected layers 1508 can be used to generate an output result from the network. The activations within the fully connected layers 1508 can be computed using matrix multiplication instead of convolution. Not all CNN implementations make use of fully connected layers 1508. For example, in some implementations the second convolutional layer 1506 can generate output for the CNN.

The convolutional layers are sparsely connected, which differs from traditional neural network configuration found in the fully connected layers 1508. Traditional neural network layers are fully connected, such that every output unit interacts with every input unit. However, the convolutional layers are sparsely connected because the output of the convolution of a field is input (instead of the respective state value of each of the nodes in the field) to the nodes of the subsequent layer, as illustrated. The kernels associated with the convolutional layers perform convolution operations, the output of which is sent to the next layer. The dimensionality reduction performed within the convolutional layers is one aspect that enables the CNN to scale to process large images.

FIG. 15B illustrates exemplary computation stages within a convolutional layer of a CNN. Input to a convolutional layer 1512 of a CNN can be processed in three stages of a convolutional layer 1514. The three stages can include a convolution stage 1516, a detector stage 1518, and a pooling stage 1520. The convolution layer 1514 can then output data to a successive convolutional layer. The final convolutional layer of the network can generate output feature map data or provide input to a fully connected layer, for example, to generate a classification value for the input to the CNN.

The convolution stage 1516 performs several convolutions in parallel to produce a set of linear activations. The convolution stage 1516 can include an affine transformation, which is any transformation that can be specified as a linear transformation plus a translation. Affine transformations include rotations, translations, scaling, and combinations of these transformations. The convolution stage computes the output of functions (e.g., neurons) that are connected to specific regions in the input, which can be determined as the local region associated with the neuron. The neurons compute a dot product between the weights of the neurons and the region in the local input to which the neurons are connected. The output from the convolution stage 1516 defines a set of linear activations that are processed by successive stages of the convolutional layer 1514.

The linear activations can be processed by a detector stage 1518. In the detector stage 1518, each linear activation is processed by a non-linear activation function. The non-linear activation function increases the nonlinear properties of the overall network without affecting the receptive fields of the convolution layer. Several types of non-linear activation functions may be used. One particular type is the rectified linear unit (ReLU), which uses an activation function defined as ƒ(x)=max (0,x), such that the activation is thresholded at zero.

The pooling stage 1520 uses a pooling function that replaces the output of the second convolutional layer 1506 with a summary statistic of the nearby outputs. The pooling function can be used to introduce translation invariance into the neural network, such that small translations to the input do not change the pooled outputs. Invariance to local translation can be useful in scenarios where the presence of a feature in the input data is more important than the precise location of the feature. Various types of pooling functions can be used during the pooling stage 1520, including max pooling, average pooling, and l2-norm pooling. Additionally, some CNN implementations do not include a pooling stage. Instead, such implementations substitute and additional convolution stage having an increased stride relative to previous convolution stages.

The output from the convolutional layer 1514 can then be processed by the next layer 1522. The next layer 1522 can be an additional convolutional layer or one of the fully connected layers 1508. For example, the first convolutional layer 1504 of FIG. 15A can output to the second convolutional layer 1506, while the second convolutional layer can output to a first layer of the fully connected layers 1508.

FIG. 16 illustrates an exemplary recurrent neural network. In a recurrent neural network (RNN), the previous state of the network influences the output of the current state of the network. RNNs can be built in a variety of ways using a variety of functions. The use of RNNs generally revolves around using mathematical models to predict the future based on a prior sequence of inputs. For example, an RNN may be used to perform statistical language modeling to predict an upcoming word given a previous sequence of words. The illustrated RNN 1600 can be described as having an input layer 1602 that receives an input vector, hidden layers 1604 to implement a recurrent function, a feedback mechanism 1605 to enable a ‘memory’ of previous states, and an output layer 1606 to output a result. The RNN 1600 operates based on time-steps. The state of the RNN at a given time step is influenced based on the previous time step via the feedback mechanism 1605. For a given time step, the state of the hidden layers 1604 is defined by the previous state and the input at the current time step. An initial input (x₁) at a first time step can be processed by the hidden layer 1604. A second input (x₂) can be processed by the hidden layer 1604 using state information that is determined during the processing of the initial input (x₁). A given state can be computed as s_(t)=ƒ(Ux_(t)+Ws_(t−1)), where U and W are parameter matrices. The function ƒ is generally a nonlinearity, such as the hyperbolic tangent function (Tan h) or a variant of the rectifier function ƒ(x)=max(0,x). However, the specific mathematical function used in the hidden layers 1604 can vary depending on the specific implementation details of the RNN 1600.

In addition to the basic CNN and RNN networks described, variations on those networks may be enabled. One example RNN variant is the long short-term memory (LSTM) RNN. LSTM RNNs are capable of learning long-term dependencies that may be necessary for processing longer sequences of language. A variant on the CNN is a convolutional deep belief network, which has a structure similar to a CNN and is trained in a manner similar to a deep belief network. A deep belief network (DBN) is a generative neural network that is composed of multiple layers of stochastic (random) variables. DBNs can be trained layer-by-layer using greedy unsupervised learning. The learned weights of the DBN can then be used to provide pre-trained neural networks by determining an optimal initial set of weights for the neural network.

FIG. 17 illustrates training and deployment of a deep neural network. Once a given network has been structured for a task the neural network is trained using a training dataset 1702. Various training frameworks have been developed to enable hardware acceleration of the training process. For example, the machine learning framework 1404 of FIG. 14 may be configured as a training framework 1704. The training framework 1704 can hook into an untrained neural network 1706 and enable the untrained neural net to be trained using the parallel processing resources described herein to generate a trained neural network 1708. To start the training process the initial weights may be chosen randomly or by pre-training using a deep belief network. The training cycle then be performed in either a supervised or unsupervised manner.

Supervised learning is a learning method in which training is performed as a mediated operation, such as when the training dataset 1702 includes input paired with the desired output for the input, or where the training dataset includes input having known output and the output of the neural network is manually graded. The network processes the inputs and compares the resulting outputs against a set of expected or desired outputs. Errors are then propagated back through the system. The training framework 1704 can adjust the weights that control the untrained neural network 1706. The training framework 1704 can provide tools to monitor how well the untrained neural network 1706 is converging towards a model suitable to generating correct answers based on known input data. The training process occurs repeatedly as the weights of the network are adjusted to refine the output generated by the neural network. The training process can continue until the neural network reaches a statistically desired accuracy associated with a trained neural network 1708. The trained neural network 1708 can then be deployed to implement any number of machine learning operations to generate an inference result 814 based on input of new data 812.

Unsupervised learning is a learning method in which the network attempts to train itself using unlabeled data. Thus, for unsupervised learning the training dataset 1702 will include input data without any associated output data. The untrained neural network 1706 can learn groupings within the unlabeled input and can determine how individual inputs are related to the overall dataset. Unsupervised training can be used to generate a self-organizing map, which is a type of trained neural network 1708 capable of performing operations useful in reducing the dimensionality of data. Unsupervised training can also be used to perform anomaly detection, which allows the identification of data points in an input dataset that deviate from the normal patterns of the data.

Variations on supervised and unsupervised training may also be employed. Semi-supervised learning is a technique in which in the training dataset 1702 includes a mix of labeled and unlabeled data of the same distribution. Incremental learning is a variant of supervised learning in which input data is continuously used to further train the model. Incremental learning enables the trained neural network 1708 to adapt to the new data 1712 without forgetting the knowledge instilled within the network during initial training.

Whether supervised or unsupervised, the training process for particularly deep neural networks may be too computationally intensive for a single compute node. Instead of using a single compute node, a distributed network of computational nodes can be used to accelerate the training process.

FIG. 18 is a block diagram illustrating distributed learning. Distributed learning is a training model that uses multiple distributed computing nodes to perform supervised or unsupervised training of a neural network. The distributed computational nodes can each include one or more host processors and one or more of the general-purpose processing nodes. As illustrated, distributed learning can involve model parallelism 1802, data parallelism 1804, or a combination of model and data parallelism 1804.

In model parallelism 1802, different computational nodes in a distributed system can perform training computations for different parts of a single network. For example, each layer of a neural network can be trained by a different processing node of the distributed system. The benefits of model parallelism include the ability to scale to particularly large models. Splitting the computations associated with different layers of the neural network enables the training of very large neural networks in which the weights of all layers would not fit into the memory of a single computational node. In some instances, model parallelism can be particularly useful in performing unsupervised training of large neural networks.

In data parallelism 1804, the different nodes of the distributed network have a complete instance of the model and each node receives a different portion of the data. The results from the different nodes are then combined. While different approaches to data parallelism are possible, data parallel training approaches all require a technique of combining results and synchronizing the model parameters between each node. Exemplary approaches to combining data include parameter averaging and update based data parallelism. Parameter averaging trains each node on a subset of the training data and sets the global parameters (e.g., weights, biases) to the average of the parameters from each node. Parameter averaging uses a central parameter server that maintains the parameter data. Update based data parallelism is similar to parameter averaging except that instead of transferring parameters from the nodes to the parameter server, the updates to the model are transferred. Additionally, update based data parallelism can be performed in a decentralized manner, where the updates are compressed and transferred between nodes.

Combined model and data parallelism 1806 can be implemented, for example, in a distributed system in which each computational node includes multiple GPUs. Each node can have a complete instance of the model with separate GPUs within each node used to train different portions of the model.

Distributed training has increased overhead relative to training on a single machine. However, the parallel processors and GPGPUs described herein can each implement various techniques to reduce the overhead of distributed training, including techniques to enable high bandwidth GPU-to-GPU data transfer and accelerated remote data synchronization.

Exemplary Machine Learning Applications

Machine learning can be applied to solve a variety of technological problems, including but not limited to computer vision, autonomous driving and navigation, speech recognition, and language processing. Computer vision has traditionally been one of the most active research areas for machine learning applications. Applications of computer vision range from reproducing human visual abilities, such as recognizing faces, to creating new categories of visual abilities. For example, computer vision applications can be configured to recognize sound waves from the vibrations induced in objects visible in a video. Parallel processor accelerated machine learning enables computer vision applications to be trained using significantly larger training datasets than previously feasible and enables inferencing systems to be deployed using low power parallel processors.

Parallel processor accelerated machine learning has autonomous driving applications including lane and road sign recognition, obstacle avoidance, navigation, and driving control. Accelerated machine learning techniques can be used to train driving models based on datasets that define the appropriate responses to specific training input. The parallel processors described herein can enable rapid training of the increasingly complex neural networks used for autonomous driving solutions and enables the deployment of low power inferencing processors in a mobile platform suitable for integration into autonomous vehicles.

Parallel processor accelerated deep neural networks have enabled machine learning approaches to automatic speech recognition (ASR). ASR includes the creation of a function that computes the most probable linguistic sequence given an input acoustic sequence. Accelerated machine learning using deep neural networks have enabled the replacement of the hidden Markov models (HMMs) and Gaussian mixture models (GMMs) previously used for ASR.

Parallel processor accelerated machine learning can also be used to accelerate natural language processing. Automatic learning procedures can make use of statistical inference algorithms to produce models that are robust to erroneous or unfamiliar input. Exemplary natural language processor applications include automatic machine translation between human languages.

The parallel processing platforms used for machine learning can be divided into training platforms and deployment platforms. Training platforms are generally highly parallel and include optimizations to accelerate multi-GPU single node training and multi-node, multi-GPU training, while deployed machine learning (e.g., inferencing) platforms generally include lower power parallel processors suitable for use in products such as cameras, autonomous robots, and autonomous vehicles.

Degradation Restoration Network (DRN)

In most conventional image restoration methods, the sub-tasks of single image super-resolution (SISR) and image denoising (DN) are learned separately, thereby resulting in difficulties when attempting to combine these two tasks together. As described further below, in various embodiments, a DRN learns SISR and DN jointly (concurrently) and therefore has a significant advantage in super-resolving noised images.

In some embodiments, an improved approach to super-resolving noisy images (e.g., LR images having unknown degradation, including, but not limited to, sensor noise and/or JPEG compression) involves input of an estimated noise map to a super-resolution (SR) convolutional neural network (CNN) subnetwork (which may also be referred to herein simply as an SR subnetwork or as a conditional SR subnetwork) of the DRN. The estimated noise map may be generated by a noise estimator (NE) CNN subnetwork (which may also be referred to herein simply as an NE subnetwork) and may be used by the SR subnetwork as guidance to adjust intermediate image features during inference processing, thereby enhancing the ability of the SR network to deal with noisy images.

In some embodiments, the conditional SR subnetwork is composed of multiple stacked and densely-connected conditional residual dense blocks (CRDBs). The CRDBs may be formed by adding a noise control layer (NCL) to a conventional residual dense block (RDB). In some embodiments, the NCL adjusts feature values of an intermediate feature map associated with the noisy input image by applying to the feature values (i) a scaling factor and (ii) an offset factor derived from an estimated noise map received as an NE subnetwork of the DRN.

In some embodiments, during training of the DRN, fast noise shifting is introduced to quickly fine-tune the DRN for real noise removal. In one embodiment, an additive noise map output by one or more noise shifting (NS) convolutional neural network (CNN) subnetworks (which may also be referred to herein individually simply as a NS subnetwork) during inference processing is combined with the estimated noise map to help the DRN deal with a wide variety of noise patterns as described further below.

Embodiments can be used in various contexts. Non-limiting examples of such contexts include image or video enhancement software running on a data processing system or a post-processing pipeline within a digital camera, a digital video camera, a simultaneous localization and mapping (SLAM) device (e.g., the Intel® RealSense™ Tracking Camera T265 or the like), and/or a digital camera with stereo image sensing technologies (e.g., the Intel® RealSense™ Depth Camera D435i or the like).

DRN Inferencing

FIG. 19 is a flow diagram illustrating degradation restoration network (DRN) inference processing, according to an embodiment. At block 1910, the DRN receives noisy images {x, x∈X} for which high resolution images (z) are to be estimated. The noisy images may be synthetic images (e.g., LR images directly downsampled from HR counterparts), real-world LR images (e.g., LR images of objects from the real world captured by a sensor) or a combination thereof.

At block 1920, noise (o₁) contained in the noisy images is estimated. In one embodiment, the noise is estimated by an NE subnetwork of the DRN. As such, the estimated noise may be generally represented as o₁=NE(x). An exemplary NE subnetwork is described further below with reference to FIG. 21.

At decision block 1930, a determination is made regarding whether noise shifting is to be performed. In one embodiment, this determination is based on whether blind or non-blind denoising is being performed. For example, when blind denoising is being performed, it may be desirable to allow an NS subnetwork of the DRN to automatically output an additive noise map to be combined with the estimated noise map. If noise shifting is to be performed, then inference processing branches to block 1940. Alternatively, when non-blind denoising is being performed it may be desirable to fix the output of the NS subnetwork to a constant value. If noise shifting is not to be performed, then inference processing continues with block 1950.

At block 1940, a determination has been made to use noise shifting. As such, the estimated noise is shifted by the NS subnetwork. In one embodiment, the shifted noise can be generally represented by o₂=NS(x)*n+o₁, where n˜N(0,75). An exemplary NS subnetwork is described further below with reference to FIG. 22B.

At block 1950, a determination has been made not to use noise shifting. As such, the estimated noise is shifted by a constant offset and the shifted noise can be generally represented by o₂=n+o₁, where n˜N(0, offset).

At block 1960, the HR image (z) is predicted. In one embodiment, the HR image is predicted by an SR subnetwork of the DRN. Generally, in the context of the present example, the output of DRN can be modeled as z=SR(x,o₂). An exemplary SR subnetwork is described further below with reference to FIG. 21 and FIG. 22A.

DRN Model

FIG. 20A is a block diagram illustrating a conditional residual dense block (CRDB) 2000, according to an embodiment. In the context of the present example, the CRDB 2000 stacks a conventional residual dense block (RDB) 2010, represented by densely connected convolutional layers 2004 a-c and 2006, and a noise control layer (NCL) 2008.

FIG. 20B is a block diagram illustrating a noise control layer (NCL) 2020 of a CRDB, according to an embodiment. NCL 2020 is one potential network structure representation of NCL 2008 of FIG. 20A. In the context of the present example, NCL 2020 receive an estimated noise map (e.g., noise 2021) as a conditional factor and transforms the output of the RDB 2010 of FIG. 20A as follows in accordance with Equation 1.1:

x _(i) =x _(i−1)*sigmoid(F ₁(noise))+F ₂(noise)  Eq. 1.1

In the context of the present example, block 2024 represents F₁ (which may be referred to herein as a scaling factor or a scaling parameter) and block 2028 represents F₂ (which may be referred to herein as an offset factor or an offset parameter) in Equation 1.1. In one embodiment, blocks 2024 and 2028 are implemented by stacking n CNN layers, and block 2024 is activated by the sigmoid function 2026 to map the scaling factor to [0, 1] (i.e., values between 0 and 1, inclusive).

As explained further below, the estimated noise map (e.g., noise 2021) can be further manually or automatically adjusted by noise shifting to facilitate removal of unknown degradation.

FIG. 21 is a block diagram illustrating a degradation restoration network (DRN) 2100, according to an embodiment. In the context of the present example, the DRN 2100 includes a super-resolution (SR) subnetwork 2110 and a noise estimator (NE) subnetwork 2120. Generally, the output of the DRN 2110 can be modeled as follows in the form of Equations 1.2-1.3:

z=F _(SR)(noise,y)  Eq. 1.2

noise=F _(NE)(y)  Eq. 1.3

In Equations 1.2-1.3, z represents clean upscaled samples (e.g., output image 2151), F_(SR) represents the SR subnetwork 2110, F_(NE) represents the NE subnetwork 2120, y represents noisy samples (e.g., input image 2101) and noise represents an estimate of the noise in the noisy samples, for example, in the form of noise level maps 2130.

According to one embodiment, the NE subnetwork 210 receives noisy samples (y) (e.g., input image 2101) as inputs, and estimates their noise level maps 2130 (noise) as outputs. The NE subnetwork 210 stacks m CNN layers. For example, the NE subnetwork 210 may include alternating convolutional and Leaky Rectified Linear Unit (LReLU) layers as illustrated in FIG. 21. For its part, the SR sub-network 2110 takes both noisy samples (y) (e.g., input image 2101) and estimated noise 2130 as inputs to generate clean upscaled samples (z) (e.g., output image 2151).

In one embodiment, the estimated noise 2130 provides the ability to adjust the SR subnetwork 2110 via multiple densely connected CRDBs 2112 a-c cascaded within each of one or more cascade blocks (CBs) 2111 a-d. In one embodiment, the estimated noise 2130 represents the strength of the noise to be removed in the input image 2101. For example, each value of the noise map refers to a corresponding pixel of the input image 2101 (e.g., an LR image). The higher the value is, the more noise the network (e.g., the NCL layers 2008 within the CRDBs 2112 a-c) removes. If the value is too high, the network would also remove textual pixels, leading to an “over-smoothed” output image 2151 (e.g., an HR image). In contrast, if the value is too low, the network would leave textual pixels as well as noise contained in the image, leading to a noisy output. As such, a stronger noise implies a smoother output. Consequently, in one embodiment, a fine-tuning method (which may be referred to as fast noise shifting) is employed as described further below with reference to FIG. 22A.

While in the context of the present example, three CRDBs 2112 a-c are shown, more or fewer CRDBs 2112 may be implemented within CBs 211 a-d based on various performance tradeoffs. Similarly, while in the context of the present example, four CBs 2111 a-d are shown, more or fewer CBs 2111 may be implemented within SR subnetwork 2110. For purposes of providing a simple way of evaluating exemplary tradeoffs for consideration in connection with selecting an appropriate number of CB s 2111 and/or CRDB s 2111 for a particular implementation, assume (x) represents the total number of CBs 221 and (y) represents the number of CRDB s 2111 within each CB 2111. Then, the impact of z=x*y (the total number of blocks) can be evaluated in relation to the particular design goals. With an increase of z, the total parameters of the SR subnetwork 2110 increases, which gives the network a stronger ability to predict more accurate clean images, but this also makes the network harder to converge (harder to train). Moreover, the increase in z also increases the inference time.

In the context of the present example, an upscale block 2140 stacks four CNN layers, for example, alternating convolutional and pixel-shift layers (PSx2), which are also referred to as depth-to-space layers.

FIG. 22A is a block diagram illustrating fine tuning of a degradation restoration network (DRN) 2200 with noise shifting, according to an embodiment. In the context of the present example, the input image 2201, the output image 2251, the CB s 2211 a-n and NE 2220 (representing the NE subnetwork) are as described above with reference to FIG. 21.

In one embodiment, a fine-tuning method (which may be referred to herein as noise shifting or fast noise shifting), can be modeled as follows in the form of Equation 1.4:

noise_(shift)=λ₀·noise+β  Eq. 1.4a

In Equation 1.4a, noise_(shift) represents the shifted noise map, which is the result of adding the shifted noise (e.g., the output of an NS 2230 a-n) to the original noise map (e.g., the output of the NE 2220), λ₀ represents a weighted constant that may be applied to the shifted noise map to balance the contribution of the shifted noise map, noise represents an estimate of the noise in the noisy samples (as estimated by NE 2220, for example), and represents an additive noise map.

In one embodiment, noise_(shift) is a value previously learned independently by each of multiple NS subnetworks 2230 a-n during a second training phase as described below with reference to FIG. 24. In such an embodiment, each of the multiple NS subnetworks 2230 a-n solve Equation 1.4a based on the learned noise_(shift) and output corresponding additive noise maps (e.g., β, β′, β″, and β″″) to be combined with a properly weighted factor of noise. As mentioned above, in one embodiment, β can also be generated manually by setting β as normally distributed noise (e.g., N(0, a)), in such a scenario, Equation 1.4a can be expressed as follows in the form of Equation 1.4b:

noise_(shift)=λ₀·noise+F _(NS)(y)·N(0,σ_(max))  Eq. 1.4b

In Equation 1.4b, F_(NS) represents an individual NS subnetwork 2230 a-n and y represents noisy samples (e.g., input image 2201).

As noted above, in one embodiment, an additive noise map output by the NS subnetworks 2230 a-n during inference processing is combined with the estimated noise map output by the NE subnetwork 2220 to help the DRN 2200 deal with a wide variety of noise patterns. For example, rather than the SR subnetwork 2210 simply relying a globally normally distributed noise map, the NS subnetworks 2230 a-n can provide input to the SR subnetwork 2210 that emphasizes some part of the noise map (e.g., a high noise region in the input image 2201) and/or understates another part of the noise map (e.g., a low noise region in the input image 2201). This is helpful, for example, when a real image has differing noise strengths contained therein, such as when a dark part of an image has higher noise than a light part.

FIG. 22B is a block diagram illustrating a noise shifting (NS) subnetwork 2260, according to an embodiment. In one embodiment, NS subnetwork 2260 is implemented by stacking n CNN layers. For example, six stacked alternating convolutional and LReLU layers, with the final layer activated by the sigmoid function 2065 to map the output to [0, 1] (i.e., values between 0 and 1, inclusive).

DRN Training

FIG. 23 is a flow diagram illustrating a first phase of degradation restoration network (DRN) training processing, according to an embodiment. As noted above, in most conventional image restoration methods, image super-resolution and image denoising are learned separately. In such prior approaches, difficulties arise when attempting to combine these two tasks after the fact. In some embodiments, DRN is trained in two phases or stages. In the context of the present example, during the first training phase, the NE subnetwork learns to estimate a noise map (o) and the SR subnetwork learns to estimate a super-resolved clean image (z) by optimizing a first loss function (see, e.g., Equation 2.1 below). In this manner, the DRN learns SISR and DN concurrently during a first training phase, thereby providing the DRN with a significant advantage in connection with super-resolving noised images.

At block 2310, the DRN creates a noisy LR image (I_(LR)) based on an image (z) retrieved from a training dataset 2311 containing clean HR image samples. For example, the retrieved clean HR image is first downsampled to create a corresponding LR image. Random noise (o) may be generated by synthesizing additive white Gaussian noise (AWGN). The random noise is then added to the LR image to create a noisy LR image. In some embodiments, the newly created noisy LR image (I_(LR)) is saved to training dataset 2311 or another training dataset for use in connection with a second training phase as described further below with reference to FIG. 24. In one embodiment, the process of retrieval, downsampling and addition of random noise can be represented as follows:

I _(HR) =z,{z,z _(i) ∈Z}.

I _(LR)=BicubicResize(z)+o

o=AWGN(σ),σ=randn(0,75)

Those skilled in the art will appreciate the clean HR image samples can include synthetic images, real-world images and/or a combination of both and may include natural scenes, and challenging urban scenes with details in different frequency bands. There are numerous publicly available training datasets and widely used benchmark datasets, including, but not limited to, those referenced in:

-   -   M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel.         Low-complexity single-image super-resolution based on         nonnegative neighbor embedding. In BMVC, 2012. (“SetS”)     -   R. Zeyde, M. Elad, and M. Protter, “On single image scale-up         using sparse-representations.” In Curves and Surfaces, pages         711-730, 2010. (“Set14”)     -   J.-B. Huang, A. Singh, and N. Ahuja. Single image         super-resolution from transformed self-exemplars. In CVPR, pages         5197-5206, 2015. (“Urban100”)     -   D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human         segmented natural images and its application to evaluating         segmentation algorithms and measuring ecological statistics. In         CVPR, pages 416-423, 2001. (“BSD100”)

As will be appreciated, blocks 2420, 2430, 2440 and 2460 effectively jointly train the NE subnetwork and the SR subnetwork on the same training data with reference to the same optimization function.

At block 2320, the NE subnetwork estimates the noise (o′) contained in the noisy LR image, which can be expressed as:

o′=NE(I _(LR))

At block 2330, the SR subnetwork estimates a super-resolved image (z′) based on the estimated noise (o′) and the noisy LR image (I_(LR)), which can be expressed as:

Z=SR(I _(LR) ,o′)

At block 2340, the objective function (e.g., the first loss function to be minimized) is computed. In one embodiment, the objective function is represented as follows in the form of Equation 2.2:

L=mean(z−z*)+λ₁*mean(|o−o| ²)+λ₂*Tv(o*)  Eq. 2.1

In Equation 2.1, the first term is the loss between the ground truth (z) and the output images (z′), representing the fidelity of the generated images, the second term is the loss of the noise, representing the accuracy of noise extraction, and the last term constrains the total variation (Tv) of the noise map, representing the smoothness of the noise map. In one embodiment, and in the benchmarks provided below, λ₁ is set to 10 and λ₂ is set to 1e−5.

At block 2350, optimization processing is performed to update weights and biases (i.e., the internal parameters (p) of the NE and SR subnetworks, where p∈NEUSR) to reduce the error. In one embodiment, the optimization algorithm employed is the Adam optimization algorithm, which is an extension to stochastic gradient decent. Notably, however, the optimization processing described herein is not limited to any particular optimization algorithm.

At decision block 2360, a determination is made regarding whether the optimization function (e.g., the loss function of Equation 2.1) has reached the minima. If so, then training of the NE and SR subnetworks is complete; otherwise, the first phase of the training processing continues by looping back to block 2310.

FIG. 24 is a flow diagram illustrating a second phase of degradation restoration network (DRN) training processing, according to an embodiment. In the context of the present example, during the second training phase, the training state of the NE subnetwork and the SR subnetwork is frozen and multiple NS subnetworks are added to the DRN to learn noise_(shift) for each CB block (e.g., CB 2211 a-n of FIG. 22A). In one embodiment, the number of trainable parameters of NS is relatively small and can be learned very fast, so this procedure may be referred to herein as fast noise shifting. For a real noisy image dataset, one cannot get the noise map by simply subtracting label and noisy samples. For example, label images are usually synthesized from multiple noisy images, resulting in non-ideal pixels remaining in label samples. As such, in one embodiment, the NS subnetworks a trained by minimizing a second loss function an example of which is expressed below in Equation 2.2.

At block 2410, the DRN retrieves an image pair, for example, a clean HR image (I_(HR)) and the corresponding noisy LR image (I_(LR)) from a training dataset 2411 {(I_(HR), I_(LR)), I_(HR)∈Z_(clean), I_(LR) ÅZ_(noisy)}. The training dataset 2411 may be the same or a different training dataset than that which was used during the first training phase; and as noted above may have been at least partially created (e.g., the corresponding noisy LR images (I_(LR)∈Z_(noisy))) or updated during the first training phase.

At block 2420, the NE subnetwork estimates the noise (o′) contained in the noisy LR image (I_(LR)) and the NS subnetwork(s) estimate the noise shift (which may be represented herein as noise_(shift) or NS(I_(LR))). In one embodiment, these estimations can be represented as follows:

o′=NE(I _(LR))

o″=o′+NS(I _(LR))

At block 2430, the SR subnetwork estimates a super-resolved image (z′) based on the shifted noise (o″), representing the combination of the estimated noise (o′) and the estimated noise shift (NS(I_(LR))) and the noisy LR image (I_(LR)), which can be expressed as:

z′=SR(I _(LR) ,o″)

At block 2440, the objective function (e.g., the second loss function to be minimized) is computed. In one embodiment, this objective function is represented as follows in the form of Equation 2.2:

L=mean(z−z′)+λ*Tv(o″)  Eq. 2.2

In Equation 2.2, the first term is the fidelity among label images and generated ones. The second term constrains the total variance (Tv) of noise_(shift). In one embodiment, and in the benchmarks provided below λ is set empirically to 1e−5.

At block 2450, optimization processing is performed to update weights and biases (i.e., the internal parameters (p) of the NS subnetwork(s)) to reduce the error, where p∈NS). In one embodiment, the optimization algorithm employed is the Adam optimization algorithm. Notably, however, the optimization processing described herein is not limited to any particular optimization algorithm.

At decision block 2460, a determination is made regarding whether the optimization function (e.g., the loss function of Equation 2.2) has reached the minima. If so, then training of the NS subnetwork(s) is complete; otherwise, the second phase of the training processing continues by looping back to block 2410.

Benchmarks

For purposes of completeness, the performance of DRN is first presented separately for SISR and DN tasks, then results of DRN are presented for the DN-SR tasks combined. The following discussion reflects the results of testing various existing models as well as DRN on well-known test datasets (e.g., SetS, Set14, Urban100 and BSD100) mentioned previously. The third-party models tested alone or in combination with others include the following:

-   R. Keys (1981). “Cubic convolution interpolation for digital image     processing”. In IEEE Transactions on Acoustics, Speech, and Signal     Processing, vol. 29, issue 6 at pp. 1153-1160, December 1981.     (“Bicubic”) -   C. Dong, C. C. Loy, K. He, and X. Tang, “Image Super-Resolution     Using Deep Convolutional Networks,” IEEE Trans. Pattern Anal. Mach.     Intell., vol. 38, no. 2, pp. 295-307, February 2016. (“SRCNN”) -   J. Kim, J. K. Lee, and K. M. Lee, “Accurate Image Super-Resolution     Using Very Deep Convolutional Networks,” in CVPR, 2016. (“VDSR”) -   W. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang, “Deep Laplacian     Pyramid Networks for Fast and Accurate Super-Resolution,” in     CVPR, 2017. (“LapSRN”) -   Y. Zhang, Y. Tian, Y. Kong, B. Zhong, and Y. Fu, “Residual Dense     Network for Image Super-Resolution,” in CVPR, 2018. (“RDN”) -   N. Ahn, B. Kang, and K. Sohn, “Fast, Accurate, and Lightweight     Super-Resolution with Cascading Residual Network,” in ECCV, 2018.     (“CARN”) -   Dabov, Kostadin; Foi, Alessandro; Katkovnik, Vladimir; Egiazarian,     Karen (16 Jul. 2007). “Image denoising by sparse 3D transform-domain     collaborative filtering”. In IEEE Transactions on Image Processing,     vol. 16, issue 8 at pp. 2080-2095. (“Block-Matching and 3D     Filtering” or “BM3D”) -   K. Zhang, W. Zuo, and L. Zhang, “FFDNet: Toward a Fast and Flexible     Solution for CNN based Image Denoising,” in IEEE Transactions on     Image Processing 2017. (“FFDNet”) -   S. Guo, Z. Yan, K. Zhang, W. Zuo, and L. Zhang, “Toward     Convolutional Blind Denoising of Real Photographs,” in IEEE     Transactions on Image Processing 2018. (“CBDNet”) -   X. Wang, K. Yu, S. Wu, J. Gu, Y. Liu, C. Dong, Y. Qiao, and C.     Change Loy, “ESRGAN: Enhanced Super-Resolution Generative     Adversarial Networks,” in ECCV, 2018. (“ESRGAN”) -   K. Zhang, W. Zuo, and L. Zhang, “Learning a Single Convolutional     Super-Resolution Network for Multiple Degradations,” in CVPR, 2018.     (“SRMD”)

Image Super-Resolution Benchmarks

FIG. 25 is a table 2500 listing Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics for image super-resolution performed by various models on well-known test datasets. Column 2505 identifies the model. Column 2510 provides PSNR/SSIM metrics for image super-resolution performed by each model on SetS. Column 2515 provides PSNR/SSIM metrics for image super-resolution performed by each model on Set14. Column 2520 provides PSNR/SSIM metrics for image super-resolution performed by each model on Urban100. Column 2525 provides PSNR/SSIM metrics for image super-resolution performed by each model on BSD100. As can be seen DRN archives comparable performance compared to these state-of-the-art models.

DND Benchmarks

FIG. 26 is a table 2600 listing PSNR and SSIM metrics for the Darmstadt Noise Dataset (DND) benchmark photo denoising test results achieved by the top five models to date. Motivated by the fact that it is an easier task to remove noise from a downsampled version of a noisy image, the inventors applied DRN to the image denoising task by super-resolving downsampled noisy images by a scaling factor of 2.

DND is an online benchmark for real photo denoising, which contains fifty large noisy photos; however, no ground truths are given. The results were obtained from DND's online submission system at https://noise.visinf.tu-darmstadt.de/benchmark/#results_srgb.

Table 2600 lists only the top five ranked DND submissions as of the date of testing. Column 2601 identifies the ranking. Column 2605 identifies the model. Column 2630 provides PSNR on standard Red Green Blue (sRGB) for each model. Column 2640 provides SSIM on sRGB for each model. As can be seen, at the time of submission, DRN(x2) ranked No. 1 for PSNR and No. 2 for SSIM among all twenty-nine submissions as of the date of testing.

Noisy Image Super-Resolution Benchmarks

For purposes of the testing results illustrated by FIGS. 27-32, the inventors synthesized AWGN on the test datasets. To make a fair comparison, traditional SR networks (e.g., VDSR) were retrained with synthetic noisy images. For DRN, noise shifting (NS) was not used for the synthetic noisy images. The results of performing image super-resolution on the noisy images created from the test datasets are provided in FIG. 27. FIGS. 28-32 (discussed further below) illustrate the visual comparison results.

FIG. 27 is a table 2700 listing PSNR and SSIM metrics for noisy image super-resolution performed by various models on well-known test datasets to which synthetic noise was added. In table 2007, PSNR of SNR is calculated on the RGB channels. Column 2705 identifies the model or combination of models. Column 2706 identifies the scaling factor (e.g., 4). Column 2707 identifies the AWGN added to the images of the test datasets. Column 2710 provides PSNR/SSIM metrics for noisy image super-resolution performed by each model on Set5. Column 2715 provides PSNR/SSIM metrics for noisy image super-resolution performed by each model on Set14. Column 2720 provides PSNR/SSIM metrics for noisy image super-resolution performed by each model on Urban100. Column 2725 provides PSNR/SSIM metrics for noisy image super-resolution performed by each model on BSD100. As can be seen DRN archived the best performance for every test dataset other than Set14.

Noisy Image Super-Resolution Visual Comparisons

The effectiveness of DRN in super-resolving real noisy photos is illustrated by FIGS. 28-32. FIGS. 28-32 illustrates x4 upscaled results for real-world photos. No quantitative metrics are given due to the lack of “ground truth” for real noisy photos.

FIG. 28 provides a visual comparison of results of noisy image super-resolution on a sample image (i.e., image ID 002) of the BSD100 dataset by various models, including a degradation restoration network (DRN) in accordance with an embodiment. Image 2800 is the real HR test image (ground truth) from the BSD100 dataset. Box 2810 is a portion of image 2800, representing the original input HR image that is to be super-resolved by a factor of 4 by the various models. Block 2811 a is a cropped version of the original HR image 2800, representing the portion within box 2810. Blocks 2811 b-h illustrate the results of super-resolving box 2810 (from an LR version of image 2800) by the following models or combination of models:

-   -   Block 2811 b—BM3D+Bicubic     -   Block 2811 c—FFDNet+CARN     -   Block 2811 d—DRN     -   Block 2811 e—CBDNet+ESRGAN     -   Block 2811 f—FFDNet+ESRGAN     -   Block 2811 g—SRMD     -   Block 2811 h—VDSR

FIG. 29 provides a visual comparison of results of noisy image super-resolution on another sample image (i.e., image ID 007) of the BSD100 dataset by various models, including a degradation restoration network (DRN) in accordance with an embodiment. Image 2900 is the real HR test image (ground truth) from the BSD100 dataset. Box 2910 is a portion of image 2900, representing the original input HR image that is to be super-resolved by a factor of 4 by the various models. Block 2911 a is a cropped version of the original HR image 2900, representing the portion within box 2910. Blocks 2911 b-h illustrate the results of super-resolving box 2910 (from an LR version of image 2900) by the following models or combination of models:

-   -   Block 2911 b—BM3D+Bicubic     -   Block 2911 c—FFDNet+CARN     -   Block 2911 d—DRN     -   Block 2911 e—CBDNet+ESRGAN     -   Block 2911 f—FFDNet+ESRGAN     -   Block 2911 g—SRMD     -   Block 2911 h—VDSR

FIG. 30 provides a visual comparison of results of noisy image super-resolution on another sample image (i.e., image ID 011) of the BSD100 dataset by various models, including a degradation restoration network (DRN) in accordance with an embodiment. Image 3000 is the real HR test image (ground truth) from the BSD100 dataset. Box 3010 is a portion of image 3000, representing the original input HR image that is to be super-resolved by a factor of 4 by the various models. Block 3011 a is a cropped version of the original HR image 3000, representing the portion within box 3010. Blocks 3011 b-h illustrate the results of super-resolving box 3010 (from an LR version of image 3000) by the following models or combination of models:

-   -   Block 3011 b—BM3D+Bicubic     -   Block 3011 c—FFDNet+CARN     -   Block 3011 d—DRN     -   Block 3011 e—CBDNet+ESRGAN     -   Block 3011 f—FFDNet+ESRGAN     -   Block 3011 g—SRMD     -   Block 3011 h—VDSR

FIG. 31 provides a visual comparison of results of noisy image super-resolution on a sample image (i.e., image ID 003 or “butterfly”) from the Set5 dataset by various models, including a degradation restoration network (DRN) in accordance with an embodiment. Image 3100 is the real HR test image (ground truth) from the Set5 dataset. Box 3110 is a portion of image 3100, representing the original input HR image that is to be super-resolved by a factor of 4 by the various models. Block 3111 a is a cropped version of the original HR image 3100, representing the portion within box 3110. Blocks 3111 b-h illustrate the results of super-resolving box 3110 (from an LR version of image 3100) by the following models or combination of models:

-   -   Block 3111 b—BM3D+Bicubic     -   Block 3111 c—FFDNet+CARN     -   Block 3111 d—DRN     -   Block 3111 e—CBDNet+ESRGAN     -   Block 3111 f—FFDNet+ESRGAN     -   Block 3111 g—SRMD     -   Block 3111 h—VDSR

FIG. 32 provides a visual comparison of results of noisy image super-resolution on a sample image (i.e., image ID 011) from the Urban100 dataset by various models, including a degradation restoration network (DRN) in accordance with an embodiment. Image 3200 is the real HR test image from the Urban100 dataset. Box 3210 is a portion of image 3200, representing the original input HR image that is to be super-resolved by a factor of 4 by the various models. Block 3211 a is a cropped version of the original HR image 3200, representing the portion within box 3210. Blocks 3211 b-h illustrate the results of super-resolving box 3210 (from an LR version of image 3200) by the following models or combination of models:

-   -   Block 3211 b—BM3D+Bicubic     -   Block 3211 c—FFDNet+CARN     -   Block 3211 d—DRN     -   Block 3211 e—CBDNet+ESRGAN     -   Block 3211 f—FFDNet+ESRGAN     -   Block 3211 g—SRMD     -   Block 3211 h—VDSR

As can be appreciated with reference to FIGS. 28-32, denoising performance impacts the quality of super-resolution. For non-blind denoising methods, such as FFDNet, the quality is acceptable only for over-smoothed denoised LR images (e.g., FIGS. 28, 29 and 30), hence FFDNet needs a carefully selected denoising level in order to retain textural details. For blind denoising methods, such as CBDNet, it intends to keep more details of the image, but it also fails to remove a some noisy pixels, which are enlarged by super-resolution (see, e.g., FIG. 32). In contrast, the outputs of DRN (fine-tuned by NS for blind denoising and super-resolution) finds a balance between denoising strength and image details as reflected by FIG. 31, for example.

Portions of various embodiments may be provided as a computer program product, which may include a computer-readable medium having stored thereon computer program instructions, which may be used to program a computer (or other electronic devices) for execution by one or more processors to perform a process according to certain embodiments. The computer-readable medium may include, but is not limited to, magnetic disks, optical disks, read-only memory (ROM), random access memory (RAM), erasable programmable read-only memory (EPROM), electrically-erasable programmable read-only memory (EEPROM), magnetic or optical cards, flash memory, or other type of computer-readable medium suitable for storing electronic instructions. Moreover, embodiments may also be downloaded as a computer program product, wherein the program may be transferred from a remote computer to a requesting computer. In some embodiments, a non-transitory computer-readable storage medium has stored thereon data representing sequences of instructions that, when executed by a processor, cause the processor to perform certain operations.

Many of the methods are described in their most basic form, but processes can be added to or deleted from any of the methods and information can be added or subtracted from any of the described messages without departing from the basic scope of the present embodiments. It will be apparent to those skilled in the art that many further modifications and adaptations can be made. The particular embodiments are not provided to limit the concept but to illustrate it. The scope of the embodiments is not to be determined by the specific examples provided above but only by the claims below.

If it is said that an element “A” is coupled to or with element “B,” element A may be directly coupled to element B or be indirectly coupled through, for example, element C. When the specification or claims state that a component, feature, structure, process, or characteristic A “causes” a component, feature, structure, process, or characteristic B, it means that “A” is at least a partial cause of “B” but that there may also be at least one other component, feature, structure, process, or characteristic that assists in causing “B.” If the specification indicates that a component, feature, structure, process, or characteristic “may”, “might”, or “could” be included, that particular component, feature, structure, process, or characteristic is not required to be included. If the specification or claim refers to “a” or “an” element, this does not mean there is only one of the described elements.

An embodiment is an implementation or example. Reference in the specification to “an embodiment,” “one embodiment,” “some embodiments,” or “other embodiments” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least some embodiments, but not necessarily all embodiments. The various appearances of “an embodiment,” “one embodiment,” or “some embodiments” are not necessarily all referring to the same embodiments. It should be appreciated that in the foregoing description of exemplary embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various novel aspects. This method of disclosure, however, is not to be interpreted as reflecting an intention that the claimed embodiments requires more features than are expressly recited in each claim. Rather, as the following claims reflect, novel aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims are hereby expressly incorporated into this description, with each claim standing on its own as a separate embodiment.

The following clauses and/or examples pertain to further embodiments or examples. Specifics in the examples may be used anywhere in one or more embodiments. The various features of the different embodiments or examples may be variously combined with some features included and others excluded to suit a variety of different applications. Examples may include subject matter such as a method, means for performing acts of the method, at least one machine-readable medium including instructions that, when performed by a machine cause the machine to perform acts of the method, or of an apparatus or system for facilitating hybrid communication according to embodiments and examples described herein.

Some embodiments pertain to Example 1 that includes an apparatus for performing image super-resolution comprising: one or more processors including a graphics processor to process data; and a memory for storage of data, including one or more images to be super-resolved. The one or more processors are to provide for super-resolving noisy images, including: estimation, by a noise estimator (NE) convolutional neural network (CNN) subnetwork of a degradation restoration network, an estimated noise map for a noisy input image; and prediction, by a super-resolution (SR) CNN subnetwork of the degradation restoration network, a clean upscaled image based on the noisy input image and the estimated noise map by, for each conditional residual dense block (CRDB) of a plurality of CRDBs stacked within each of one or more cascade blocks representing the SR CNN subnetwork, adjusting, by a noise control layer of the CRDB that follows a stacked set of a plurality of residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the noisy input image by applying to the feature values (i) a scaling factor and (ii) an offset factor derived from the estimated noise map.

Example 2 includes the subject matter of Example 1, wherein a first cascade block of the one or more cascade blocks makes use of hierarchical features from each of the plurality of CRDBs within the first cascade block by providing all of the plurality of CRDBs within the first cascade block with access to original inputs to and outputs of all preceding CRDBs of the plurality of CRDBs within the first cascade block.

Example 3 includes the subject matter of Examples 1-2, further including determination by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map to be combined with the estimated noise map input to a corresponding cascade block of the one or more cascade blocks.

Example 4 includes the subject matter of Examples 1-3, wherein the scaling factor is determined during training of the degradation restoration network.

Example 5 includes the subject matter of Examples 1-4, further including training of the degradation restoration network by performing a multi-phase training process, wherein a first training phase of the multi-phase training process includes concurrently training the SR CNN subnetwork to upscale training images and the NE CNN subnetwork to estimate a noise map associated with the training images, wherein the training images comprise synthetic noisy images.

Example 6 includes the subject matter of Examples 1-5, further including minimization of a first loss function by performing a first optimization process during the first training phase, wherein the first loss function is represented by

L=mean(z−z′)+λ₁*mean(|o−o′| ²)+λ₂*Tv(o)

where,

-   -   z represents high resolution ground truth images;     -   z′ represents clean upscaled images predicted by the SR CNN         subnetwork during the first training phase;     -   represents a noise map estimated by the NE CNN subnetwork during         the training phase;     -   o′ represents random additive white Gaussian noise added to         downsampled versions of the high resolution ground truth images         to produce the synthetic noisy images;     -   λ₁ is a first configurable parameter;     -   λ₂ is a second configurable parameter; and     -   Tv represents total variance.

Example 7 includes the subject matter of Examples 1-6, further including after the first training phase is complete, performance of a second training phase, including learning, by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map for a corresponding cascade block of the one or more cascade blocks by performing a fast noise shifting process based on the synthetic noisy images.

Example 8 includes the subject matter of Examples 1-7, further including minimization of a second loss function by performing a second optimization process during the second training phase, wherein the second loss function is represented by

L=mean(z−z′)+A*Tv(o″)

where,

-   -   o″ is a noise shift, generated by an NS CNN subnetwork of the         one or more NS CNN subnetworks, based on (i) a noise map         estimated by the NE CNN subnetwork based on the synthetic noisy         images and (ii) an additive noise map output by the NS CNN         subnetwork;     -   z represents high resolution ground truth images;     -   z′ represents clean upscaled images predicted by the SR CNN         subnetwork based on the synthetic noisy images and the noise         shift;     -   λ is a configurable parameter; and     -   Tv represents total variance.

Some embodiments pertain to Example 9 that includes a method for performing image super-resolution comprising: estimating, by a noise estimator (NE) convolutional neural network (CNN) subnetwork of a degradation restoration network implemented on data processing system, an estimated noise map for a noisy input image; and predicting, by a super-resolution (SR) CNN subnetwork of the degradation restoration network, a clean upscaled image based on the noisy input image and the estimated noise map by, for each conditional residual dense block (CRDB) of a plurality of CRDBs stacked within each of one or more cascade blocks representing the SR CNN subnetwork, adjusting, by a noise control layer of the CRDB that follows a stacked set of a plurality of residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the noisy input image by applying to the feature values (i) a scaling factor and (ii) an offset factor derived from the estimated noise map.

Example 10 includes the subject matter of Example 9, further comprising determining by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map to be combined with the estimated noise map input to a corresponding cascade block of the one or more cascade blocks.

Example 11 includes the subject matter of Examples 9-10, wherein the scaling factor is determined during training of the degradation restoration network.

Example 12 includes the subject matter of Examples 9-11, further comprising training the degradation restoration network by performing a multi-phase training process, wherein a first training phase of the multi-phase training process includes concurrently training the SR CNN subnetwork to upscale training images and the NE CNN subnetwork to estimate a noise map associated with the training images, wherein the training images comprise synthetic noisy images.

Example 13 includes the subject matter of Examples 9-12, further comprising minimizing a first loss function by performing a first optimization process during the first training phase, wherein the first loss function is represented by

L=mean(z−z′)+λ₁*mean(|o−o′| ²)+λ₂*Tv(o)

where,

-   -   z represents high resolution ground truth images;     -   z′ represents clean upscaled images predicted by the SR CNN         subnetwork during the first training phase;     -   represents a noise map estimated by the NE CNN subnetwork during         the training phase;     -   o′ represents random additive white Gaussian noise added to         downsampled versions of the high resolution ground truth images         to produce the synthetic noisy images;     -   λ₁ is a first configurable parameter;     -   λ₂ is a second configurable parameter; and     -   Tv represents total variance.

Example 14 includes the subject matter of Examples 9-13, further comprising after the first training phase is complete, performing a second training phase, including learning, by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map for a corresponding cascade block of the one or more cascade blocks by performing a fast noise shifting process based on the synthetic noisy images.

Example 15 includes the subject matter of Examples 9-14, further comprising minimizing a second loss function by performing a second optimization process during the second training phase, wherein the second loss function is represented by

L=mean(z−z′)+λ*Tv(o″)

where,

-   -   o″ is a noise shift, generated by an NS CNN subnetwork of the         one or more NS CNN subnetworks, based on (i) a noise map         estimated by the NE CNN subnetwork based on the synthetic noisy         images and (ii) an additive noise map output by the NS CNN         subnetwork;     -   z represents high resolution ground truth images;     -   z′ represents clean upscaled images predicted by the SR CNN         subnetwork based on the synthetic noisy images and the noise         shift;     -   λ is a configurable parameter; and     -   Tv represents total variance.

Some embodiments pertain to Example 16 that includes a system for performing image super-resolution, the system comprising one or more processors, including a graphics processor, to process data; and a memory for storage of data, one or more images to be super-resolved. The one or more processors are to provide for super-resolving noisy images, including: estimation, by a noise estimator (NE) convolutional neural network (CNN) subnetwork of a degradation restoration network, an estimated noise map for a noisy input image; and prediction, by a super-resolution (SR) CNN subnetwork of the degradation restoration network, a clean upscaled image based on the noisy input image and the estimated noise map by, for each conditional residual dense block (CRDB) of a plurality of CRDBs stacked within each of one or more cascade blocks representing the SR CNN subnetwork, adjusting, by a noise control layer of the CRDB that follows a stacked set of a plurality of residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the noisy input image by applying to the feature values (i) a scaling factor and (ii) an offset factor derived from the estimated noise map.

Example 17 includes the subject matter of Example 16, wherein a first cascade block of the one or more cascade blocks makes use of hierarchical features from each of the plurality of CRDBs within the first cascade block by providing all of the plurality of CRDBs within the first cascade block with access to original inputs to and outputs of all preceding CRDBs of the plurality of CRDBs within the first cascade block.

Example 18 includes the subject matter of Examples 16-17, further including determination by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map to be combined with the estimated noise map input to a corresponding cascade block of the one or more cascade blocks.

Example 19 includes the subject matter of Examples 16-18, wherein the scaling factor is determined during training of the degradation restoration network.

Example 20 includes the subject matter of Examples 16-19, further including training of the degradation restoration network by performing a multi-phase training process, wherein a first training phase of the multi-phase training process includes concurrently training the SR CNN subnetwork to upscale training images and the NE CNN subnetwork to estimate a noise map associated with the training images, wherein the training images comprise synthetic noisy images.

Example 21 includes the subject matter of Examples 16-20, further including minimization of a first loss function by performing a first optimization process during the first training phase, wherein the first loss function is represented by

L=mean(z−z′)+λ₁*mean(|o−o′| ²)+λ₂*Tv(o)

where,

-   -   z represents high resolution ground truth images;     -   z′ represents clean upscaled images predicted by the SR CNN         subnetwork during the first training phase;     -   represents a noise map estimated by the NE CNN subnetwork during         the training phase;     -   o′ represents random additive white Gaussian noise added to         downsampled versions of the high resolution ground truth images         to produce the synthetic noisy images;     -   λ₁ is a first configurable parameter;     -   λ₂ is a second configurable parameter; and     -   Tv represents total variance.

Example 22 includes the subject matter of Examples 16-21, further including after the first training phase is complete, performance of a second training phase, including learning, by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map for a corresponding cascade block of the one or more cascade blocks by performing a fast noise shifting process based on the synthetic noisy images.

Example 23 includes the subject matter of Examples 16-22, further including minimization of a second loss function by performing a second optimization process during the second training phase, wherein the second loss function is represented by

L=mean(z−z′)+λ*Tv(o″)

where,

-   -   o″ is a noise shift, generated by an NS CNN subnetwork of the         one or more NS CNN subnetworks, based on (i) a noise map         estimated by the NE CNN subnetwork based on the synthetic noisy         images and (ii) an additive noise map output by the NS CNN         subnetwork;     -   z represents high resolution ground truth images;     -   z′ represents clean upscaled images predicted by the SR CNN         subnetwork based on the synthetic noisy images and the noise         shift;     -   λ is a configurable parameter; and     -   Tv represents total variance.

Some embodiments pertain to Example 24 that includes an apparatus for sparse three-dimensional (3D) convolution acceleration comprising: a noise estimator (NE) means of a degradation restoration network for estimating an estimated noise map for a noisy input image; and a super-resolution (SR) means of the degradation restoration network for predicting a clean upscaled image based on the noisy input image and the estimated noise map by, for each conditional residual dense block (CRDB) of a plurality of CRDBs stacked within each of one or more cascade blocks representing the SR means, adjusting, by a noise control layer of the CRDB that follows a stacked set of a plurality of residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the noisy input image by applying to the feature values (i) a scaling factor and (ii) an offset factor derived from the estimated noise map.

Example 25 includes the subject matter of Example 24, wherein a first cascade block of the one or more cascade blocks makes use of hierarchical features from each of the plurality of CRDBs within the first cascade block by providing all of the plurality of CRDBs within the first cascade block with access to original inputs to and outputs of all preceding CRDBs of the plurality of CRDBs within the first cascade block.

Example 26 includes the subject matter of Examples 24-25, further including one or more noise shifting (NS) means for determining an additive noise map to be combined with the estimated noise map input to a corresponding cascade block of the one or more cascade blocks.

Example 27 includes the subject matter of Examples 24-26, wherein the scaling factor is determined during training of the degradation restoration network.

Example 28 includes the subject matter of Examples 24-27, further including a means for training of the degradation restoration network by performing a multi-phase training process, wherein a first training phase of the multi-phase training process includes concurrently training the SR means to upscale training images and the NE means to estimate a noise map associated with the training images, wherein the training images comprise synthetic noisy images.

Example 29 includes the subject matter of Examples 24-28, further including a means for minimizing a first loss function by performing a first optimization process during the first training phase, wherein the first loss function is represented by

L=mean(z−z′)+λ₁*mean(|o−o′| ²)+λ₂*Tv(o)

where,

-   -   z represents high resolution ground truth images;     -   z′ represents clean upscaled images predicted by the SR means         during the first training phase;     -   o represents a noise map estimated by the NE means during the         training phase;     -   o′ represents random additive white Gaussian noise added to         downsampled versions of the high resolution ground truth images         to produce the synthetic noisy images;     -   λ₁ is a first configurable parameter;     -   λ₂ is a second configurable parameter; and     -   Tv represents total variance.

Example 30 includes the subject matter of Examples 24-29, further including after the first training phase is complete, performance of a second training phase, including a means for learning, by each NS means of the one or more NS means of the degradation restoration network, an additive noise map for a corresponding cascade block of the one or more cascade blocks by performing a fast noise shifting process based on the synthetic noisy images.

Example 31 includes the subject matter of Examples 24-30, further including a means for minimization of a second loss function by performing a second optimization process during the second training phase, wherein the second loss function is represented by

L=mean(z−z′)+λ*Tv(o″)

where,

-   -   o″ is a noise shift, generated by an NS means of the one or more         NS means, based on (i) a noise map estimated by the NE means         based on the synthetic noisy images and (ii) an additive noise         map output by the NS means;     -   z represents high resolution ground truth images;     -   z′ represents clean upscaled images predicted by the SR means         based on the synthetic noisy images and the noise shift;     -   λ is a configurable parameter; and     -   Tv represents total variance.

Some embodiments pertain to Example 32 that includes one or more non-transitory computer-readable storage mediums having stored thereon executable computer program instructions that, when executed by one or more processors of a data processing system, cause the one or more processors to perform operations comprising: estimating, by a noise estimator (NE) convolutional neural network (CNN) subnetwork of a degradation restoration network implemented on data processing system, an estimated noise map for a noisy input image; and predicting, by a super-resolution (SR) CNN subnetwork of the degradation restoration network, a clean upscaled image based on the noisy input image and the estimated noise map by, for each conditional residual dense block (CRDB) of a plurality of CRDBs stacked within each of one or more cascade blocks representing the SR CNN subnetwork, adjusting, by a noise control layer of the CRDB that follows a stacked set of a plurality of residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the noisy input image by applying to the feature values (i) a scaling factor and (ii) an offset factor derived from the estimated noise map.

Example 33 includes the subject matter of Example 32, wherein the operations further comprise determining by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map to be combined with the estimated noise map input to a corresponding cascade block of the one or more cascade blocks.

Example 34 includes the subject matter of Examples 32-33, wherein the scaling factor is determined during training of the degradation restoration network.

Example 35 includes the subject matter of Examples 32-34, wherein the operations further comprise training the degradation restoration network by performing a multi-phase training process, wherein a first training phase of the multi-phase training process includes concurrently training the SR CNN subnetwork to upscale training images and the NE CNN subnetwork to estimate a noise map associated with the training images, wherein the training images comprise synthetic noisy images.

Example 36 includes the subject matter of Examples 32-35, wherein the operations further comprise minimizing a first loss function by performing a first optimization process during the first training phase, wherein the first loss function is represented by

L=mean(z−z′)+λ₁*mean(|o−o′| ²)+λ₂*Tv(o)

where,

-   -   z represents high resolution ground truth images;     -   z′ represents clean upscaled images predicted by the SR CNN         subnetwork during the first training phase;     -   represents a noise map estimated by the NE CNN subnetwork during         the training phase;     -   o′ represents random additive white Gaussian noise added to         downsampled versions of the high resolution ground truth images         to produce the synthetic noisy images;     -   λ₁ is a first configurable parameter;     -   λ₂ is a second configurable parameter; and     -   Tv represents total variance.

Example 37 includes the subject matter of Examples 32-36, wherein the operations further comprise after the first training phase is complete, performing a second training phase, including learning, by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map for a corresponding cascade block of the one or more cascade blocks by performing a fast noise shifting process based on the synthetic noisy images.

Example 38 includes the subject matter of Examples 32-37, wherein the operations further comprise minimizing a second loss function by performing a second optimization process during the second training phase, wherein the second loss function is represented by

L=mean(z−z′)+λ*Tv(o″)

where,

-   -   o″ is a noise shift, generated by an NS CNN subnetwork of the         one or more NS CNN subnetworks, based on (i) a noise map         estimated by the NE CNN subnetwork based on the synthetic noisy         images and (ii) an additive noise map output by the NS CNN         subnetwork;     -   z represents high resolution ground truth images;     -   z′ represents clean upscaled images predicted by the SR CNN         subnetwork based on the synthetic noisy images and the noise         shift;     -   λ is a configurable parameter; and     -   Tv represents total variance.

Example 39 includes least one non-transitory or tangible machine-readable medium comprising a plurality of instructions, when executed on a computing device, to implement or perform a method as claimed in any of claims or Examples 9-15.

Example 40 includes least one machine-readable medium comprising a plurality of instructions, when executed on a computing device, to implement or perform a method as claimed in any of claims or Examples 9-15.

Example 41 includes a system comprising a mechanism to implement or perform a method as claimed in any of claims or Examples 9-15.

Example 42 includes an apparatus comprising means for performing a method as claimed in any of claims or Examples 9-15.

Example 43 includes a computing device arranged to implement or perform a method as claimed in any of claims or Examples 9-15.

Example 44 includes a communications device arranged to implement or perform a method as claimed in any of claims or Examples 9-15.

Example 45 includes at least one machine-readable medium comprising a plurality of instructions, when executed on a computing device, to implement or perform a method or realize an apparatus as described in any preceding Example.

Example 46 includes at least one non-transitory or tangible machine-readable medium comprising a plurality of instructions, when executed on a computing device, to implement or perform a method or realize an apparatus as described in any preceding Example.

Example 47 includes a system comprising a mechanism to implement or perform a method or realize an apparatus as described in any preceding Example.

Example 48 includes an apparatus comprising means to perform a method as described in any preceding Example.

Example 49 includes a computing device arranged to implement or perform a method or realize an apparatus as described in any preceding Example.

Example 50 includes a communications device arranged to implement or perform a method or realize an apparatus as described in any preceding Example.

The drawings and the forgoing description give examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be split into multiple functional elements. Elements from one embodiment may be added to another embodiment. For example, orders of processes described herein may be changed and are not limited to the manner described herein. Moreover, the actions of any flow diagram need not be implemented in the order shown; nor do all of the acts necessarily need to be performed. Also, those acts that are not dependent on other acts may be performed in parallel with the other acts. The scope of embodiments is by no means limited by these specific examples. Numerous variations, whether explicitly given in the specification or not, such as differences in structure, dimension, and use of material, are possible. The scope of embodiments is at least as broad as given by the following claims. 

1-20. (canceled)
 21. An apparatus for performing image super-resolution comprising: one or more processors including a graphics processor to process data; and a memory for storage of data, including one or more images to be super-resolved; wherein the one or more processors are to provide for super-resolving noisy images, including: estimation, by a noise estimator (NE) convolutional neural network (CNN) subnetwork of a degradation restoration network, an estimated noise map for a noisy input image; and prediction, by a super-resolution (SR) CNN subnetwork of the degradation restoration network, a clean upscaled image based on the noisy input image and the estimated noise map by, for each conditional residual dense block (CRDB) of a plurality of CRDBs stacked within each of one or more cascade blocks representing the SR CNN subnetwork, adjusting, by a noise control layer of the CRDB that follows a stacked set of a plurality of residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the noisy input image by applying to the feature values (i) a scaling factor and (ii) an offset factor derived from the estimated noise map.
 22. The apparatus of claim 21, wherein a first cascade block of the one or more cascade blocks makes use of hierarchical features from each of the plurality of CRDBs within the first cascade block by providing all of the plurality of CRDBs within the first cascade block with access to original inputs to and outputs of all preceding CRDBs of the plurality of CRDBs within the first cascade block.
 23. The apparatus of claim 21, further including determination by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map to be combined with the estimated noise map input to a corresponding cascade block of the one or more cascade blocks.
 24. The apparatus of claim 21, wherein the scaling factor is determined during training of the degradation restoration network.
 25. The apparatus of claim 21, further including training of the degradation restoration network by performing a multi-phase training process, wherein a first training phase of the multi-phase training process includes concurrently training the SR CNN subnetwork to upscale training images and the NE CNN subnetwork to estimate a noise map associated with the training images, wherein the training images comprise synthetic noisy images.
 26. The apparatus of claim 25, further including minimization of a first loss function by performing a first optimization process during the first training phase, wherein the first loss function is represented by L=mean(z−z′)+λ₁*mean(|o−o′| ²)+λ₂*Tv(o) where, z represents high resolution ground truth images; z′ represents clean upscaled images predicted by the SR CNN subnetwork during the first training phase; o represents a noise map estimated by the NE CNN subnetwork during the training phase; o′ represents random additive white Gaussian noise added to downsampled versions of the high resolution ground truth images to produce the synthetic noisy images; λ₁ is a first configurable parameter; λ₂ is a second configurable parameter; and Tv represents total variance.
 27. The apparatus of claim 25, further including after the first training phase is complete, performance of a second training phase, including learning, by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map for a corresponding cascade block of the one or more cascade blocks by performing a fast noise shifting process based on the synthetic noisy images.
 28. The apparatus of claim 27, further including minimization of a second loss function by performing a second optimization process during the second training phase, wherein the second loss function is represented by L=mean(z−z′)+*Tv(o″) where, o″ is a noise shift, generated by an NS CNN subnetwork of the one or more NS CNN subnetworks, based on (i) a noise map estimated by the NE CNN subnetwork based on the synthetic noisy images and (ii) an additive noise map output by the NS CNN subnetwork; z represents high resolution ground truth images; z′ represents clean upscaled images predicted by the SR CNN subnetwork based on the synthetic noisy images and the noise shift; λ is a configurable parameter; and Tv represents total variance.
 29. A method for performing image super-resolution comprising: estimating, by a noise estimator (NE) convolutional neural network (CNN) subnetwork of a degradation restoration network implemented on data processing system, an estimated noise map for a noisy input image; and predicting, by a super-resolution (SR) CNN subnetwork of the degradation restoration network, a clean upscaled image based on the noisy input image and the estimated noise map by, for each conditional residual dense block (CRDB) of a plurality of CRDBs stacked within each of one or more cascade blocks representing the SR CNN subnetwork, adjusting, by a noise control layer of the CRDB that follows a stacked set of a plurality of residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the noisy input image by applying to the feature values (i) a scaling factor and (ii) an offset factor derived from the estimated noise map.
 30. The method of claim 29, further comprising determining by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map to be combined with the estimated noise map input to a corresponding cascade block of the one or more cascade blocks.
 31. The method of claim 29, wherein the scaling factor is determined during training of the degradation restoration network.
 32. The method of claim 29, further comprising training the degradation restoration network by performing a multi-phase training process, wherein a first training phase of the multi-phase training process includes concurrently training the SR CNN subnetwork to upscale training images and the NE CNN subnetwork to estimate a noise map associated with the training images, wherein the training images comprise synthetic noisy images.
 33. The method of claim 32, further comprising minimizing a first loss function by performing a first optimization process during the first training phase, wherein the first loss function is represented by L=mean(z−z′)+λ₁*mean(|o−o′| ²)+λ₂*Tv(o) where, z represents high resolution ground truth images; z′ represents clean upscaled images predicted by the SR CNN subnetwork during the first training phase; o represents a noise map estimated by the NE CNN subnetwork during the training phase; o′ represents random additive white Gaussian noise added to downsampled versions of the high resolution ground truth images to produce the synthetic noisy images; λ₁ is a first configurable parameter; λ₂ is a second configurable parameter; and Tv represents total variance.
 34. The method of claim 32, further comprising after the first training phase is complete, performing a second training phase, including learning, by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map for a corresponding cascade block of the one or more cascade blocks by performing a fast noise shifting process based on the synthetic noisy images.
 35. The method of claim 34, further comprising minimizing a second loss function by performing a second optimization process during the second training phase, wherein the second loss function is represented by L=mean(z−z′)+λ*Tv(o″) where, o″ is a noise shift, generated by an NS CNN subnetwork of the one or more NS CNN subnetworks, based on (i) a noise map estimated by the NE CNN subnetwork based on the synthetic noisy images and (ii) an additive noise map output by the NS CNN subnetwork; z represents high resolution ground truth images; z′ represents clean upscaled images predicted by the SR CNN subnetwork based on the synthetic noisy images and the noise shift; λ is a configurable parameter; and Tv represents total variance.
 36. A non-transitory computer-readable storage medium embodying a set of instructions, which when executed by one or more processors, including a graphics processor, causes the one or more processors to: estimate, by a noise estimator (NE) convolutional neural network (CNN) subnetwork of a degradation restoration network, an estimated noise map for a noisy input image; and predict, by a super-resolution (SR) CNN subnetwork of the degradation restoration network, a clean upscaled image based on the noisy input image and the estimated noise map by, for each conditional residual dense block (CRDB) of a plurality of CRDBs stacked within each of one or more cascade blocks representing the SR CNN subnetwork, adjusting, by a noise control layer of the CRDB that follows a stacked set of a plurality of residual dense blocks of the CRDB, feature values of an intermediate feature map associated with the noisy input image by applying to the feature values (i) a scaling factor and (ii) an offset factor derived from the estimated noise map.
 37. The non-transitory computer-readable storage medium of claim 36, wherein the set of instructions further causes the one or more processors to determine by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map to be combined with the estimated noise map input to a corresponding cascade block of the one or more cascade blocks.
 38. The non-transitory computer-readable storage medium of claim 36, wherein the scaling factor is determined during training of the degradation restoration network.
 39. The non-transitory computer-readable storage medium of claim 36, wherein the set of instructions further causes the one or more processors to train the degradation restoration network by performing a multi-phase training process, wherein a first training phase of the multi-phase training process includes concurrently training the SR CNN subnetwork to upscale (raining images and the NE CNN subnetwork to estimate a noise map associated with the training images, wherein the training images comprise synthetic noisy images.
 40. The non-transitory computer-readable storage medium of claim 39, wherein the set of instructions further causes the one or more processors to after the first training phase is complete, perform a second training phase, including learning, by each noise shifting (NS) CNN subnetwork of one or more NS CNN subnetworks of the degradation restoration network, an additive noise map for a corresponding cascade block of the one or more cascade blocks by performing a fast noise shifting process based on the synthetic noisy images. 